Recruiting Bond

https://recruitingbond.com

Founded :

2024

Type :

Services

Size :

0-10

Stage :

Bootstrapped

About

Recruiting Bond is a global leader in Recruitment Process Outsourcing (RPO), Executive Search, Headhunting, Talent Mapping, and Workforce Consulting. Founded by Pavan B, we are on a mission to power businesses through transformative talent strategies that scale teams, accelerate innovation, and unlock human potential.

With a presence across 25+ industries—from IT, Healthcare, and FinTech to Gaming, BioTech, and Web3—we specialize in hiring that drives outcomes. Our domain expertise spans high-growth startups to Fortune 500 companies, delivering elite CXO and leadership talent, strategic workforce solutions, and inclusive hiring at scale.

We help businesses:

✔️ Hire the right leaders and builders

✔️ Scale globally with speed and precision

✔️ Build talent-first roadmaps from MVP to IPO

Whether you're launching, scaling, or transforming—Recruiting Bond is your strategic partner in talent.

📨 Let’s build the future—together: https://recruitingbond.c

We help businesses:

✔️ Hire the right leaders and builders

✔️ Scale globally with speed and precision

✔️ Build talent-first roadmaps from MVP to IPO

Whether you're launching, scaling, or transforming—Recruiting Bond is your strategic partner in talent.

📨 Let’s build the future—together: https://recruitingbond.c

Tech stack

HR Tech

Candid answers by the company

What does the company do?

What is the location preference of jobs?

We help businesses:

✔️ Hire the right leaders and builders

✔️ Scale globally with speed and precision

✔️ Build talent-first roadmaps from MVP to IPO

Whether you're launching, scaling, or transforming—Recruiting Bond is your strategic partner in talent.

Company social profiles

Jobs at Recruiting Bond

Data Scientist

Public Listed - Product Based company

Agency job

via Recruiting Bond by Pavan Kumar

Bengaluru (Bangalore)

4 - 8 yrs

₹25L - ₹70L / yr

Data Science

data platforms

Data-flow analysis

Data pipelines

AI Infrastructure

+28 more

🤖 Data Scientist – Frontier AI for Data Platforms & Distributed Systems (4–8 Years)

Experience: 4–8 Years

Location: Bengaluru (On-site / Hybrid)

Company: Publicly Listed, Global Product Platform

🧠 About the Mission

We are building a Top 1% AI-Native Engineering & Data Organization — from first principles.

This is not incremental improvement.

This is a full-stack transformation of a large-scale enterprise into an AI-native data platform company.

We are re-architecting:

Legacy systems → AI-native architectures
Static pipelines → autonomous, self-healing systems
Data platforms → intelligent, learning systems
Software workflows → agentic execution layers

This is the kind of shift you would expect from companies like Google or Microsoft —

Except here, you will build it from day zero and scale it globally.

🧠 The Opportunity: This role sits at the intersection of three high-impact domains:

1. Frontier AI Systems: Large Language Models (LLMs), Small Language Models (SLMs), and Agentic AI

2. Data Platforms: Warehouses, Lakehouses, Streaming Systems, Query Engines

3. Distributed Systems: High-throughput, low-latency, multi-region infrastructure

We are building systems where:

Data platforms optimize themselves using ML/LLMs
Pipelines are autonomous, self-healing, and adaptive
Queries are generated, optimized, and executed intelligently
Infrastructure learns from usage and evolves continuously

This is: AI as the control plane for data infrastructure

🧩 What You’ll Work On

You will design and build AI-native systems deeply embedded inside data infrastructure.

1. AI-Native Data Platforms

Build LLM-powered interfaces:
Natural language → SQL / pipelines / transformations
Design semantic data layers:
Embeddings, vector search, knowledge graphs
Develop AI copilots:
For data engineers, analysts, and platform users

2. Autonomous Data Pipelines

Build self-healing ETL/ELT systems using AI agents
Create pipelines that:
Detect anomalies in real time
Automatically debug failures
Dynamically optimize transformations

3. Intelligent Query & Compute Optimization

Apply ML/LLMs to:
Query planning and execution
Cost-based optimization using learned models
Workload prediction and scheduling
Build systems that:
Learn from query patterns
Continuously improve performance and cost efficiency

4. Distributed Data + AI Infrastructure

Architect systems operating at:
Billions of events per day
Petabyte-scale data
Work with:
Distributed compute engines (Spark / Flink / Ray class systems)
Streaming systems (Kafka-class infra)
Vector databases and hybrid retrieval systems

5. Learning Systems & Feedback Loops

Build closed-loop AI systems:
Execution → feedback → model updates
Develop:
Continual learning pipelines
Online learning systems for infra optimization
Experimentation frameworks (A/B, bandits, eval pipelines)

6. LLM & Agentic Systems (Infra-Aware)

Build agents that understand data systems
Enable:
Autonomous pipeline debugging
Root cause analysis for infra failures
Intelligent orchestration of data workflows

🧠 What We’re Looking For

Core Foundations

Strong grounding in:
Machine Learning, Deep Learning, NLP
Statistics, optimization, probabilistic systems
Distributed systems fundamentals
Deep understanding of:
Transformer architectures
Modern LLM ecosystems

Hands-On Expertise

Experience building:
LLM / GenAI systems (RAG, fine-tuning, embeddings)
Data platforms (warehouse, lake, lakehouse architectures)
Distributed pipelines and compute systems
Strong programming skills:
Python (ML/AI stack)
SQL (deep understanding — query planning, optimization mindset)

Systems Thinking (Critical)

You think in systems, not components.

Built or worked on:
Large-scale data pipelines
High-throughput distributed systems
Low-latency, high-concurrency architectures
Understand:
Query optimization and execution
Data partitioning, indexing, caching
Trade-offs in distributed systems

🔥 What Sets You Apart (Top 1%)

Built AI-powered data platforms or infra systems in production
Designed or contributed to:
Query engines / optimizers
Data observability / lineage systems
AI-driven infra or AIOps platforms
Experience with:
Multi-modal AI (logs, metrics, traces, text)
Agentic AI systems
Autonomous infrastructure
Worked on systems at scale comparable to:
Google (BigQuery-like systems)
Meta (real-time analytics infra)
Snowflake / Databricks (lakehouse architectures)

🧬 Ideal Background (Not Mandatory)

We often see strong candidates from:

Data infrastructure or platform engineering teams
AI-first startups or research-driven environments
High-scale product companies

Experience building:

Internal platforms used by 1000s of engineers
Systems serving millions of users / high throughput workloads
Multi-region, distributed cloud systems

🧠 The Kind of Problems You’ll Solve

Can LLMs replace traditional query optimizers?
How do we build self-healing data pipelines at scale?
Can data systems learn from every query and improve automatically?
How do we embed reasoning and planning into infrastructure layers?
What does a fully autonomous data platform look like?

Background: We Commonly See (But Not Limited To)

Our team often includes engineers from top-tier institutions and strong research or product backgrounds, including:

Leading engineering schools in India and globally
Engineers with experience in top product companies, AI startups, or research-driven environments
That said, we care far more about demonstrated ability, depth, and impact than pedigree alone.

🤖 Data Scientist – Frontier AI for Data Platforms & Distributed Systems (4–8 Years)

Experience: 4–8 Years

Location: Bengaluru (On-site / Hybrid)

Company: Publicly Listed, Global Product Platform

🧠 About the Mission

We are building a Top 1% AI-Native Engineering & Data Organization — from first principles.

This is not incremental improvement.

This is a full-stack transformation of a large-scale enterprise into an AI-native data platform company.

We are re-architecting:

Legacy systems → AI-native architectures
Static pipelines → autonomous, self-healing systems
Data platforms → intelligent, learning systems
Software workflows → agentic execution layers

This is the kind of shift you would expect from companies like Google or Microsoft —

Except here, you will build it from day zero and scale it globally.

🧠 The Opportunity: This role sits at the intersection of three high-impact domains:

1. Frontier AI Systems: Large Language Models (LLMs), Small Language Models (SLMs), and Agentic AI

2. Data Platforms: Warehouses, Lakehouses, Streaming Systems, Query Engines

3. Distributed Systems: High-throughput, low-latency, multi-region infrastructure

We are building systems where:

Data platforms optimize themselves using ML/LLMs
Pipelines are autonomous, self-healing, and adaptive
Queries are generated, optimized, and executed intelligently
Infrastructure learns from usage and evolves continuously

This is: AI as the control plane for data infrastructure

🧩 What You’ll Work On

You will design and build AI-native systems deeply embedded inside data infrastructure.

1. AI-Native Data Platforms

Build LLM-powered interfaces:
Natural language → SQL / pipelines / transformations
Design semantic data layers:
Embeddings, vector search, knowledge graphs
Develop AI copilots:
For data engineers, analysts, and platform users

2. Autonomous Data Pipelines

Build self-healing ETL/ELT systems using AI agents
Create pipelines that:
Detect anomalies in real time
Automatically debug failures
Dynamically optimize transformations

3. Intelligent Query & Compute Optimization

Apply ML/LLMs to:
Query planning and execution
Cost-based optimization using learned models
Workload prediction and scheduling
Build systems that:
Learn from query patterns
Continuously improve performance and cost efficiency

4. Distributed Data + AI Infrastructure

Architect systems operating at:
Billions of events per day
Petabyte-scale data
Work with:
Distributed compute engines (Spark / Flink / Ray class systems)
Streaming systems (Kafka-class infra)
Vector databases and hybrid retrieval systems

5. Learning Systems & Feedback Loops

Build closed-loop AI systems:
Execution → feedback → model updates
Develop:
Continual learning pipelines
Online learning systems for infra optimization
Experimentation frameworks (A/B, bandits, eval pipelines)

6. LLM & Agentic Systems (Infra-Aware)

Build agents that understand data systems
Enable:
Autonomous pipeline debugging
Root cause analysis for infra failures
Intelligent orchestration of data workflows

🧠 What We’re Looking For

Core Foundations

Strong grounding in:
Machine Learning, Deep Learning, NLP
Statistics, optimization, probabilistic systems
Distributed systems fundamentals
Deep understanding of:
Transformer architectures
Modern LLM ecosystems

Hands-On Expertise

Experience building:
LLM / GenAI systems (RAG, fine-tuning, embeddings)
Data platforms (warehouse, lake, lakehouse architectures)
Distributed pipelines and compute systems
Strong programming skills:
Python (ML/AI stack)
SQL (deep understanding — query planning, optimization mindset)

Systems Thinking (Critical)

You think in systems, not components.

Built or worked on:
Large-scale data pipelines
High-throughput distributed systems
Low-latency, high-concurrency architectures
Understand:
Query optimization and execution
Data partitioning, indexing, caching
Trade-offs in distributed systems

🔥 What Sets You Apart (Top 1%)

Built AI-powered data platforms or infra systems in production
Designed or contributed to:
Query engines / optimizers
Data observability / lineage systems
AI-driven infra or AIOps platforms
Experience with:
Multi-modal AI (logs, metrics, traces, text)
Agentic AI systems
Autonomous infrastructure
Worked on systems at scale comparable to:
Google (BigQuery-like systems)
Meta (real-time analytics infra)
Snowflake / Databricks (lakehouse architectures)

🧬 Ideal Background (Not Mandatory)

We often see strong candidates from:

Data infrastructure or platform engineering teams
AI-first startups or research-driven environments
High-scale product companies

Experience building:

Internal platforms used by 1000s of engineers
Systems serving millions of users / high throughput workloads
Multi-region, distributed cloud systems

🧠 The Kind of Problems You’ll Solve

Can LLMs replace traditional query optimizers?
How do we build self-healing data pipelines at scale?
Can data systems learn from every query and improve automatically?
How do we embed reasoning and planning into infrastructure layers?
What does a fully autonomous data platform look like?

Background: We Commonly See (But Not Limited To)

Our team often includes engineers from top-tier institutions and strong research or product backgrounds, including:

Leading engineering schools in India and globally
Engineers with experience in top product companies, AI startups, or research-driven environments
That said, we care far more about demonstrated ability, depth, and impact than pedigree alone.

Tech Lead (Backend / Fullstack | 7–10 Years)

Public Listed - Product Based Company

Agency job

via Recruiting Bond by Pavan Kumar

Bengaluru (Bangalore)

7 - 10 yrs

₹40L - ₹75L / yr

Java

Python

Go Programming (Golang)

NodeJS (Node.js)

Database Design

+36 more

🧭 Tech Lead (Backend / Fullstack | 7–10 Years)

Location: Bangalore (On-Site, Hybrid)

Company Type: Public-Listed Product Company

We’re Building a “Top 1% Engineering Org”

We’re building a high-talent-density, AI-first R&D organization from scratch — inside a publicly listed company undergoing a full-scale transformation.

Think:

→ Rewriting legacy systems into AI-native architectures

→ Embedding LLMs + Agentic AI into core workflows

→ Reimagining platforms, infra, and data systems for the next decade

This is the kind of shift you’d expect from Google, Microsoft, or Meta —

Except you get to build it from day 0 → scale it globally.

About the Role / Team

We are building a next-generation AI-first R&D organization in Bengaluru, focused on solving complex problems across LLMs, Agentic AI systems, distributed computing, and enterprise-scale architectures.

This initiative is part of a publicly listed global company investing heavily in AI-driven transformation, re-architecting its platforms into intelligent, autonomous systems powered by large language models, workflows, and decision engines.

You will be working on:

Agentic AI systems & LLM-powered workflows
Distributed, scalable backend systems
Enterprise-grade AI platforms
Automation-first engineering environments

🚀 The Mandate

Lead execution of mission-critical systems while staying hands-on — bridging architecture and delivery.

🧩 What You’ll Do

Own end-to-end delivery of complex engineering initiatives (0→1, 1→N)
Design systems across backend + frontend (if fullstack)
Translate ambiguous problems into structured technical solutions
Drive engineering best practices, code quality, and velocity
Mentor engineers and elevate team performance
Collaborate with stakeholders on roadmap and execution strategy

🧠 What We’re Looking For

Strong experience in backend systems + optional frontend frameworks
Proven ability to lead projects and deliver at scale
Solid understanding of system design and architecture patterns
Ability to balance speed vs quality vs scalability trade-offs
Strong communication and leadership without authority
Strong coding skills in Python / Java / Go / Node.js
Solid understanding of data structures, system design basics, and backend architecture
Experience building scalable APIs and services
Familiarity or curiosity around AI/LLMs, async systems, or event-driven design
Strong debugging, problem-solving, and ownership mindset

Nice to Have

Experience integrating LLMs, vector databases, or AI pipelines
Contributions to architecture at scale
Experience with Agentic AI / LLM orchestration frameworks
Background in product engineering or platform companies
Exposure to global-scale systems (millions of users / high throughput)

🔥 What Sets You Apart

Experience leading platform builds or major system rewrites
Exposure to AI systems, LLM integrations, or intelligent workflows
Built platforms used by millions of users / high-throughput systems
Experience with event-driven systems, stream processing, or infra platforms
Prior work on AI/ML platforms, model serving, or intelligent systems

Background: We Commonly See (But Not Limited To)

Our team often includes engineers from top-tier institutions and strong research or product company or DeepTech or AI Product backgrounds, including:
Leading engineering schools in India and globally
Engineers with experience in top product companies, AI startups, or research-driven environments
That said, we care far more about demonstrated ability, depth, and impact than pedigree alone.

🧭 Tech Lead (Backend / Fullstack | 7–10 Years)

Location: Bangalore (On-Site, Hybrid)

Company Type: Public-Listed Product Company

We’re Building a “Top 1% Engineering Org”

We’re building a high-talent-density, AI-first R&D organization from scratch — inside a publicly listed company undergoing a full-scale transformation.

Think:

→ Rewriting legacy systems into AI-native architectures

→ Embedding LLMs + Agentic AI into core workflows

→ Reimagining platforms, infra, and data systems for the next decade

This is the kind of shift you’d expect from Google, Microsoft, or Meta —

Except you get to build it from day 0 → scale it globally.

About the Role / Team

You will be working on:

Agentic AI systems & LLM-powered workflows
Distributed, scalable backend systems
Enterprise-grade AI platforms
Automation-first engineering environments

🚀 The Mandate

Lead execution of mission-critical systems while staying hands-on — bridging architecture and delivery.

🧩 What You’ll Do

Own end-to-end delivery of complex engineering initiatives (0→1, 1→N)
Design systems across backend + frontend (if fullstack)
Translate ambiguous problems into structured technical solutions
Drive engineering best practices, code quality, and velocity
Mentor engineers and elevate team performance
Collaborate with stakeholders on roadmap and execution strategy

🧠 What We’re Looking For

Strong experience in backend systems + optional frontend frameworks
Proven ability to lead projects and deliver at scale
Solid understanding of system design and architecture patterns
Ability to balance speed vs quality vs scalability trade-offs
Strong communication and leadership without authority
Strong coding skills in Python / Java / Go / Node.js
Solid understanding of data structures, system design basics, and backend architecture
Experience building scalable APIs and services
Familiarity or curiosity around AI/LLMs, async systems, or event-driven design
Strong debugging, problem-solving, and ownership mindset

Nice to Have

Experience integrating LLMs, vector databases, or AI pipelines
Contributions to architecture at scale
Experience with Agentic AI / LLM orchestration frameworks
Background in product engineering or platform companies
Exposure to global-scale systems (millions of users / high throughput)

🔥 What Sets You Apart

Experience leading platform builds or major system rewrites
Exposure to AI systems, LLM integrations, or intelligent workflows
Built platforms used by millions of users / high-throughput systems
Experience with event-driven systems, stream processing, or infra platforms
Prior work on AI/ML platforms, model serving, or intelligent systems

Background: We Commonly See (But Not Limited To)

Our team often includes engineers from top-tier institutions and strong research or product company or DeepTech or AI Product backgrounds, including:
Leading engineering schools in India and globally
Engineers with experience in top product companies, AI startups, or research-driven environments
That said, we care far more about demonstrated ability, depth, and impact than pedigree alone.

Principal Engineer

at Recruiting Bond

2 candid answers

Posted by Pavan Kumar

Bengaluru (Bangalore), Mumbai

10 - 16 yrs

₹75L - ₹130L / yr

Distributed Systems

Microservices

Enterprise architecture

System Design & Architecture

Event-Driven Architecture

+29 more

🚨 We’re Building a “Top 1% Engineering Org”

We’re building a high-talent-density, AI-first R&D organization from scratch — inside a publicly listed company undergoing a full-scale transformation.

Think:

→ Rewriting legacy systems into AI-native architectures

→ Embedding LLMs + Agentic AI into core workflows

→ Reimagining platforms, infra, and data systems for the next decade

This is the kind of shift you’d expect from Google, Microsoft, or Meta —

Except you get to build it from day 0 → scale it globally.

About the Role / Team

We are building a next-generation AI-first R&D organization in Bengaluru, focused on solving complex problems across LLMs, Agentic AI systems, distributed computing, and enterprise-scale architectures.

This initiative is part of a publicly listed global company investing heavily in AI-driven transformation, re-architecting its platforms into intelligent, autonomous systems powered by large language models, workflows, and decision engines.

You will be working on:

Agentic AI systems & LLM-powered workflows
Distributed, scalable backend systems
Enterprise-grade AI platforms
Automation-first engineering environments

🚀 The Mandate

Own and evolve the technical backbone of an AI-first enterprise platform.

You will define architecture across LLM-powered systems, distributed services, and data platforms — and lead critical transformations from legacy → AI-native systems.

🧩 What You’ll Do

Architect large-scale distributed systems powering AI-driven workflows
Lead 0→1 and 1→N platform builds (LLM integrations, agentic systems, orchestration layers)
Redesign legacy systems into scalable, modular, AI-native architectures
Drive system design excellence across teams (APIs, infra, observability, reliability)
Make high-stakes decisions on trade-offs (latency, cost, scalability, model performance)
Mentor senior engineers and influence engineering culture/org standards
Partner with product, data, and leadership on long-term technical strategy

🧠 What We’re Looking For

Proven track record building high-scale backend or platform systems
Deep expertise in distributed systems, microservices, cloud (AWS/GCP/Azure)
Strong exposure to data systems/infra / Data / real-time architectures
Experience or strong interest in LLMs, GenAI, or AI system design
Exceptional system design, abstraction, and problem-solving ability
High ownership mindset — you think in terms of systems, not tickets
Strong coding skills in Python / Java / Go / Node.js
Solid understanding of data structures, system design basics, and backend architecture
Experience building scalable APIs and services
Familiarity or curiosity around AI/LLMs, async systems, or event-driven design
Strong debugging, problem-solving, and ownership mindset
Solve hard system problems (latency, scale, reliability)
Drive cross-team technical decisions and standards
Mentor senior engineers and influence org-wide architecture
Design large-scale distributed systems and backend platforms
Mentorship & Technical Leadership
Expertise in system design, scalability, and performance optimization

Nice to Have

Experience integrating LLMs, vector databases, or AI pipelines
Contributions to architecture at scale
Experience with Agentic AI / LLM orchestration frameworks
Background in product engineering or platform companies
Exposure to global-scale systems (millions of users / high throughput)

🔥 What Sets You Apart

Built platforms used by millions of users / high-throughput systems
Experience with event-driven systems, stream processing, or infra platforms
Prior work on AI/ML platforms, model serving, or intelligent systems

🚨 We’re Building a “Top 1% Engineering Org”

We’re building a high-talent-density, AI-first R&D organization from scratch — inside a publicly listed company undergoing a full-scale transformation.

Think:

→ Rewriting legacy systems into AI-native architectures

→ Embedding LLMs + Agentic AI into core workflows

→ Reimagining platforms, infra, and data systems for the next decade

This is the kind of shift you’d expect from Google, Microsoft, or Meta —

Except you get to build it from day 0 → scale it globally.

About the Role / Team

You will be working on:

Agentic AI systems & LLM-powered workflows
Distributed, scalable backend systems
Enterprise-grade AI platforms
Automation-first engineering environments

🚀 The Mandate

Own and evolve the technical backbone of an AI-first enterprise platform.

You will define architecture across LLM-powered systems, distributed services, and data platforms — and lead critical transformations from legacy → AI-native systems.

🧩 What You’ll Do

Architect large-scale distributed systems powering AI-driven workflows
Lead 0→1 and 1→N platform builds (LLM integrations, agentic systems, orchestration layers)
Redesign legacy systems into scalable, modular, AI-native architectures
Drive system design excellence across teams (APIs, infra, observability, reliability)
Make high-stakes decisions on trade-offs (latency, cost, scalability, model performance)
Mentor senior engineers and influence engineering culture/org standards
Partner with product, data, and leadership on long-term technical strategy

🧠 What We’re Looking For

Proven track record building high-scale backend or platform systems
Deep expertise in distributed systems, microservices, cloud (AWS/GCP/Azure)
Strong exposure to data systems/infra / Data / real-time architectures
Experience or strong interest in LLMs, GenAI, or AI system design
Exceptional system design, abstraction, and problem-solving ability
High ownership mindset — you think in terms of systems, not tickets
Strong coding skills in Python / Java / Go / Node.js
Solid understanding of data structures, system design basics, and backend architecture
Experience building scalable APIs and services
Familiarity or curiosity around AI/LLMs, async systems, or event-driven design
Strong debugging, problem-solving, and ownership mindset
Solve hard system problems (latency, scale, reliability)
Drive cross-team technical decisions and standards
Mentor senior engineers and influence org-wide architecture
Design large-scale distributed systems and backend platforms
Mentorship & Technical Leadership
Expertise in system design, scalability, and performance optimization

Nice to Have

Experience integrating LLMs, vector databases, or AI pipelines
Contributions to architecture at scale
Experience with Agentic AI / LLM orchestration frameworks
Background in product engineering or platform companies
Exposure to global-scale systems (millions of users / high throughput)

🔥 What Sets You Apart

Built platforms used by millions of users / high-throughput systems
Experience with event-driven systems, stream processing, or infra platforms
Prior work on AI/ML platforms, model serving, or intelligent systems

Engineering Manager - Fullstack AI Technologies

at Recruiting Bond

2 candid answers

Posted by Pavan Kumar

Mumbai, Navi Mumbai

10 - 15 yrs

₹55L - ₹80L / yr

Distributed Systems

Systems design

Systems architecture

High-level design

LLD

+77 more

Location: Mumbai, Maharashtra, India

Sector: Technology, Information & Media

Company Size: 500 - 1,000 Employees

Employment: Full-Time, Permanent

Experience: 10 - 14 Years (Engineering Leadership)

Level: Engineering Manager / Group EM

ABOUT THIS MANDATE :

Recruiting Bond has been exclusively retained by one of India's most prominent and well-established digital platform organisations operating at the intersection of Technology, Information, and Media to identify and place an exceptional Engineering Manager who can lead engineering teams through an enterprise-wide AI adoption and digital transformation agenda.

This is a high-impact, hands-on leadership role at the nexus of people, product, and technology. The organisation is executing one of the most ambitious AI transformation programmes in its sector and this Engineering Manager will be a core driver of that change. You will lead multiple squads, own engineering delivery end-to-end, embed AI tooling and practices into the team's DNA, and shape the engineering culture of tomorrow.

We are seeking leaders who code when it matters, who build systems and teams with equal conviction, and who view AI not as a trend but as a fundamental shift in how great software is built.

THE OPPORTUNITY AT A GLANCE :

AI-First Engineering Culture :

Own AI adoption across your squads - from LLM tooling integration to automation-first delivery workflows. Make AI a default, not an afterthought.

Hands-On Engineering Leadership :

Stay close to the code. Lead architecture reviews, unblock engineers, and set the technical bar - not just the management agenda.

People & Org Builder :

Grow engineers into leaders. Build squads of 615 across functions. Drive hiring, career frameworks, and a culture of psychological safety.

KEY RESPONSIBILITIES :

1. Hands-On Technical Engagement :

Remain deeply embedded in the technical work participate in design reviews, architecture decisions, and critical code reviews
Set and uphold the engineering quality bar : performance benchmarks, security standards, test coverage, and release quality
Provide technical direction on backend platform strategy, API design, service decomposition, and data architecture
Identify and resolve systemic technical debt and architectural risks across team-owned services
Unblock engineers by diving into complex problems debugging, pair programming, and system analysis when it matters
Own key technical decisions in collaboration with Tech Leads and Principal Engineers; balance pragmatism with long-term sustainability

2. AI Adoption, Integration & Transformation (2026 Mandate) :

Define and execute the team's AI adoption roadmap - from developer tooling to product-facing AI features
Champion the integration of GenAI tools (GitHub Copilot, Cursor, Claude, ChatGPT) across the full engineering workflow coding, testing, documentation, incident response
Embed LLM-powered capabilities into the product : recommendation engines, intelligent search, conversational interfaces, content generation, and predictive systems
Lead evaluation and adoption of AI-assisted SDLC practices : automated code review, AI-generated test suites, intelligent observability, and anomaly detection
Partner with Data Science and ML Platform teams to productionise ML models with robust MLOps pipelines
Build team literacy in prompt engineering, RAG (Retrieval-Augmented Generation), and AI agent frameworks
Create an experimentation culture : run structured AI pilots, measure productivity impact, and scale what works
Stay ahead of the AI tooling landscape and advise senior leadership on strategic AI investments and engineering implications

3. People Leadership & Team Development :

Lead, manage, and grow squads of 6 - 15 engineers across seniority levels (L2 through L6 / Junior through Staff)
Conduct structured 1 : 1s, career growth conversations, and development planning with every direct report
Design and execute personalised AI upskilling programmes ensure every engineer develops practical AI fluency by end of 2026
Build and maintain a high-performance team culture : clarity of ownership, accountability, fast feedback loops, and psychological safety
Drive performance management fairly and rigorously recognise top performers, manage underperformance constructively
Lead technical hiring end-to-end : define job requirements, conduct bar-raising interviews, and make data-driven hire decisions
Contribute to engineering career frameworks and level definitions in partnership with the VP / Director of Engineering

4. Engineering Delivery & Execution Excellence :

Own end-to-end delivery for multiple product squads from planning and scoping through production release and post-launch stability
Implement and refine agile delivery frameworks (Scrum, Kanban, Shape Up) calibrated to squad needs and product cadence
Drive predictable delivery : maintain healthy sprint velocity, manage WIP limits, and ensure dependency resolution across teams.
Establish and own engineering KPIs : DORA metrics (deployment frequency, lead time, MTTR, change failure rate), uptime SLOs, and velocity trends
Lead incident management : build blameless post-mortem culture, own RCA processes, and drive systemic reliability improvements
Balance technical debt repayment with feature velocity negotiate prioritisation transparently with Product leadership

5. Strategic Leadership & Cross-Functional Influence :

Serve as the primary engineering partner for Product, Design, Data, and Business stakeholders translate ambiguity into executable engineering plans
Participate in quarterly roadmap planning, capacity forecasting, and OKR definition for engineering teams
Represent engineering in leadership forums articulate technical constraints, risks, and opportunities in business terms
Contribute to org-wide engineering strategy : platform investments, build-vs-buy decisions, and shared infrastructure priorities
Build relationships across geographies (Mumbai HQ + distributed teams) to maintain alignment and delivery cohesion
Act as a culture carrier and ambassador for engineering excellence, innovation, and responsible AI use

AI TRANSFORMATION LEADERSHIP 2026 EXPECTATIONS :

In 2026, Engineering Managers at this organisation are expected to be active architects of AI transformation not passive observers. The following outlines the specific AI leadership expectations for this role :

AI Developer Productivity

Drive measurable uplift in developer velocity through AI tooling adoption. Target : 30%+ reduction in code review cycle time and 40%+ increase in test coverage automation by Q3 2026.

LLM & GenAI Product Features

Own delivery of GenAI-powered product capabilities : intelligent content, semantic search, personalisation, and conversational UX in production, at scale.

AI-Augmented Observability

Implement AI-driven monitoring and anomaly detection pipelines. Reduce MTTR by leveraging predictive alerting, intelligent runbooks, and auto-remediation scripts.

Team AI Fluency :

Build mandatory AI literacy across all engineering levels.
Every engineer understands prompt engineering basics, AI ethics guardrails, and responsible AI deployment practices.

Responsible AI Governance :

Partner with Security, Legal, and Data Privacy to ensure all AI deployments meet compliance standards, bias mitigation requirements, and explainability benchmarks.

TECHNOLOGY STACK & DOMAIN FAMILIARITY REQUIRED :

Languages: Java/ Go/ Python/ Node.js /PHP /Rust (must be hands-on in at least 2)
Cloud: AWS / GCP / Azure (multi-cloud exposure strongly preferred)
AI & GenAI: OpenAI / Anthropic / Gemini APIs /LangChain /LlamaIndex / RAG / Vector DBs / GitHub
Copilot: Cursor /Hugging Face
Containers: Docker /Kubernetes /Helm /Service Mesh (Istio / Linkerd)
Databases: PostgreSQL /MongoDB / Redis / Cassandra / Elasticsearch / Pinecone (Vector DB)
Messaging: Apache Kafka /RabbitMQ /AWS SQS/SNS /Google Pub/Sub
MLOps & DataOps: MLflow /Kubeflow / SageMaker / Vertex AI /Airflow /dbt
Observability: Datadog /Prometheus /Grafana /OpenTelemetry / Jaeger /ELK Stack
CI/CD & IaC: GitHub Actions ArgoCD / Jenkins / Terraform /Ansible /Backstage (IDP)

QUALIFICATIONS & CANDIDATE PROFILE :

Education :

B.E. / B.Tech or M.E. / M.Tech from a Tier-I or Tier-II Institution - CS, IS, ECE, AI/ML streams strongly preferred
Demonstrated engineering depth and leadership impact may complement institution pedigree

Experience :

10 to 14 years of progressive engineering experience, with at least 3 years in a formal Engineering Manager or equivalent people-leadership role
Proven track record of managing and scaling engineering teams (615+ engineers) in a fast-growing SaaS or digital product environment
Hands-on backend engineering background must be able to read, write, and critique production code
Direct experience driving AI/ML feature delivery or AI tooling adoption within engineering organisations
Exposure across start-up, mid-size, and large-scale product organisations, preferred adaptability is a core requirement
Strong CS fundamentals: distributed systems, algorithms, system design, and software architecture
Demonstrated career stability minimum of 2 years of average tenure per organisation.

The Ideal Engineering Manager in 2026 :

Leads with context, not control, empowers engineers while maintaining accountability and quality
Is fluent in both people language and technical language, switches registers naturally with engineers and executives alike
Sees AI as a force multiplier for the team, not a threat. Actively experiments with and advocates for AI tooling
Measures success by team outcomes, not personal output. Takes pride in what the team ships, not what they build alone
Creates feedback loops obsessively between product and engineering, between seniors and juniors, between metrics and decisions
Has strong opinions, loosely held, brings conviction to discussions but updates on evidence
Invests in engineering excellence as seriously as delivery velocity knows that quality and speed are not opposites

WHY THIS ROLE STANDS APART :

AI Transformation at Scale :

Lead one of the most significant AI adoption programmes in India's digital media sector.
Our decisions will shape how hundreds of engineers work in 2026 and beyond.

Hands-On & Strategic Balance :

A rare EM role that actively encourages technical depth.
Stay close to the code while owning the people agenda - the best of both worlds.

Established Platform, Real Scale :

5001,000 engineers, proven product-market fit, and the org maturity to execute.
This is not a greenfield startup gamble it is a serious company with serious ambition.

Clear Leadership Growth Path :

A visible, direct path toward Director / VP of Engineering.
Senior leadership is invested in growing its next generation of technology executives.

Location: Mumbai, Maharashtra, India

Sector: Technology, Information & Media

Company Size: 500 - 1,000 Employees

Employment: Full-Time, Permanent

Experience: 10 - 14 Years (Engineering Leadership)

Level: Engineering Manager / Group EM

ABOUT THIS MANDATE :

We are seeking leaders who code when it matters, who build systems and teams with equal conviction, and who view AI not as a trend but as a fundamental shift in how great software is built.

THE OPPORTUNITY AT A GLANCE :

AI-First Engineering Culture :

Own AI adoption across your squads - from LLM tooling integration to automation-first delivery workflows. Make AI a default, not an afterthought.

Hands-On Engineering Leadership :

Stay close to the code. Lead architecture reviews, unblock engineers, and set the technical bar - not just the management agenda.

People & Org Builder :

Grow engineers into leaders. Build squads of 615 across functions. Drive hiring, career frameworks, and a culture of psychological safety.

KEY RESPONSIBILITIES :

1. Hands-On Technical Engagement :

Remain deeply embedded in the technical work participate in design reviews, architecture decisions, and critical code reviews
Set and uphold the engineering quality bar : performance benchmarks, security standards, test coverage, and release quality
Provide technical direction on backend platform strategy, API design, service decomposition, and data architecture
Identify and resolve systemic technical debt and architectural risks across team-owned services
Unblock engineers by diving into complex problems debugging, pair programming, and system analysis when it matters
Own key technical decisions in collaboration with Tech Leads and Principal Engineers; balance pragmatism with long-term sustainability

2. AI Adoption, Integration & Transformation (2026 Mandate) :

Define and execute the team's AI adoption roadmap - from developer tooling to product-facing AI features
Champion the integration of GenAI tools (GitHub Copilot, Cursor, Claude, ChatGPT) across the full engineering workflow coding, testing, documentation, incident response
Embed LLM-powered capabilities into the product : recommendation engines, intelligent search, conversational interfaces, content generation, and predictive systems
Lead evaluation and adoption of AI-assisted SDLC practices : automated code review, AI-generated test suites, intelligent observability, and anomaly detection
Partner with Data Science and ML Platform teams to productionise ML models with robust MLOps pipelines
Build team literacy in prompt engineering, RAG (Retrieval-Augmented Generation), and AI agent frameworks
Create an experimentation culture : run structured AI pilots, measure productivity impact, and scale what works
Stay ahead of the AI tooling landscape and advise senior leadership on strategic AI investments and engineering implications

3. People Leadership & Team Development :

Lead, manage, and grow squads of 6 - 15 engineers across seniority levels (L2 through L6 / Junior through Staff)
Conduct structured 1 : 1s, career growth conversations, and development planning with every direct report
Design and execute personalised AI upskilling programmes ensure every engineer develops practical AI fluency by end of 2026
Build and maintain a high-performance team culture : clarity of ownership, accountability, fast feedback loops, and psychological safety
Drive performance management fairly and rigorously recognise top performers, manage underperformance constructively
Lead technical hiring end-to-end : define job requirements, conduct bar-raising interviews, and make data-driven hire decisions
Contribute to engineering career frameworks and level definitions in partnership with the VP / Director of Engineering

4. Engineering Delivery & Execution Excellence :

Own end-to-end delivery for multiple product squads from planning and scoping through production release and post-launch stability
Implement and refine agile delivery frameworks (Scrum, Kanban, Shape Up) calibrated to squad needs and product cadence
Drive predictable delivery : maintain healthy sprint velocity, manage WIP limits, and ensure dependency resolution across teams.
Establish and own engineering KPIs : DORA metrics (deployment frequency, lead time, MTTR, change failure rate), uptime SLOs, and velocity trends
Lead incident management : build blameless post-mortem culture, own RCA processes, and drive systemic reliability improvements
Balance technical debt repayment with feature velocity negotiate prioritisation transparently with Product leadership

5. Strategic Leadership & Cross-Functional Influence :

Serve as the primary engineering partner for Product, Design, Data, and Business stakeholders translate ambiguity into executable engineering plans
Participate in quarterly roadmap planning, capacity forecasting, and OKR definition for engineering teams
Represent engineering in leadership forums articulate technical constraints, risks, and opportunities in business terms
Contribute to org-wide engineering strategy : platform investments, build-vs-buy decisions, and shared infrastructure priorities
Build relationships across geographies (Mumbai HQ + distributed teams) to maintain alignment and delivery cohesion
Act as a culture carrier and ambassador for engineering excellence, innovation, and responsible AI use

AI TRANSFORMATION LEADERSHIP 2026 EXPECTATIONS :

AI Developer Productivity

Drive measurable uplift in developer velocity through AI tooling adoption. Target : 30%+ reduction in code review cycle time and 40%+ increase in test coverage automation by Q3 2026.

LLM & GenAI Product Features

Own delivery of GenAI-powered product capabilities : intelligent content, semantic search, personalisation, and conversational UX in production, at scale.

AI-Augmented Observability

Implement AI-driven monitoring and anomaly detection pipelines. Reduce MTTR by leveraging predictive alerting, intelligent runbooks, and auto-remediation scripts.

Team AI Fluency :

Build mandatory AI literacy across all engineering levels.
Every engineer understands prompt engineering basics, AI ethics guardrails, and responsible AI deployment practices.

Responsible AI Governance :

Partner with Security, Legal, and Data Privacy to ensure all AI deployments meet compliance standards, bias mitigation requirements, and explainability benchmarks.

TECHNOLOGY STACK & DOMAIN FAMILIARITY REQUIRED :

Languages: Java/ Go/ Python/ Node.js /PHP /Rust (must be hands-on in at least 2)
Cloud: AWS / GCP / Azure (multi-cloud exposure strongly preferred)
AI & GenAI: OpenAI / Anthropic / Gemini APIs /LangChain /LlamaIndex / RAG / Vector DBs / GitHub
Copilot: Cursor /Hugging Face
Containers: Docker /Kubernetes /Helm /Service Mesh (Istio / Linkerd)
Databases: PostgreSQL /MongoDB / Redis / Cassandra / Elasticsearch / Pinecone (Vector DB)
Messaging: Apache Kafka /RabbitMQ /AWS SQS/SNS /Google Pub/Sub
MLOps & DataOps: MLflow /Kubeflow / SageMaker / Vertex AI /Airflow /dbt
Observability: Datadog /Prometheus /Grafana /OpenTelemetry / Jaeger /ELK Stack
CI/CD & IaC: GitHub Actions ArgoCD / Jenkins / Terraform /Ansible /Backstage (IDP)

QUALIFICATIONS & CANDIDATE PROFILE :

Education :

B.E. / B.Tech or M.E. / M.Tech from a Tier-I or Tier-II Institution - CS, IS, ECE, AI/ML streams strongly preferred
Demonstrated engineering depth and leadership impact may complement institution pedigree

Experience :

10 to 14 years of progressive engineering experience, with at least 3 years in a formal Engineering Manager or equivalent people-leadership role
Proven track record of managing and scaling engineering teams (615+ engineers) in a fast-growing SaaS or digital product environment
Hands-on backend engineering background must be able to read, write, and critique production code
Direct experience driving AI/ML feature delivery or AI tooling adoption within engineering organisations
Exposure across start-up, mid-size, and large-scale product organisations, preferred adaptability is a core requirement
Strong CS fundamentals: distributed systems, algorithms, system design, and software architecture
Demonstrated career stability minimum of 2 years of average tenure per organisation.

The Ideal Engineering Manager in 2026 :

Leads with context, not control, empowers engineers while maintaining accountability and quality
Is fluent in both people language and technical language, switches registers naturally with engineers and executives alike
Sees AI as a force multiplier for the team, not a threat. Actively experiments with and advocates for AI tooling
Measures success by team outcomes, not personal output. Takes pride in what the team ships, not what they build alone
Creates feedback loops obsessively between product and engineering, between seniors and juniors, between metrics and decisions
Has strong opinions, loosely held, brings conviction to discussions but updates on evidence
Invests in engineering excellence as seriously as delivery velocity knows that quality and speed are not opposites

WHY THIS ROLE STANDS APART :

AI Transformation at Scale :

Lead one of the most significant AI adoption programmes in India's digital media sector.
Our decisions will shape how hundreds of engineers work in 2026 and beyond.

Hands-On & Strategic Balance :

A rare EM role that actively encourages technical depth.
Stay close to the code while owning the people agenda - the best of both worlds.

Established Platform, Real Scale :

5001,000 engineers, proven product-market fit, and the org maturity to execute.
This is not a greenfield startup gamble it is a serious company with serious ambition.

Clear Leadership Growth Path :

A visible, direct path toward Director / VP of Engineering.
Senior leadership is invested in growing its next generation of technology executives.

Tech Lead - Backend

at Recruiting Bond

2 candid answers

Posted by Pavan Kumar

Bengaluru (Bangalore)

6 - 12 yrs

₹40L - ₹55L / yr

Python

Go Programming (Golang)

NodeJS (Node.js)

Java

Distributed Systems

+27 more

NOW HIRING · WORLD-CLASS TALENT Backend Tech Lead (Senior Level Engineering Leadership)

Placed by Recruiting Bond on behalf of a Confidential Digital Platform Leader

📍Location: Bengaluru, India (Hybrid / On-Site)

🏢Sector: Technology, Information & Media

👥Company Size: 500 – 1,000 Employees

💼Employment: Full-Time, Permanent

🎯Experience: 6 – 9 Years (Backend Engineering)

🚀 Level: Tech Lead

ABOUT THIS MANDATE

Recruiting Bond has been exclusively retained by one of India's most well-established digital platform organisations — a company operating at the intersection of Technology, Information, and Media — to identify and place a world-class Backend Tech Lead who can drive a transformational engineering agenda at scale.

This is not an ordinary role. The organisation is executing a high-stakes, large-scale modernisation of its backend infrastructure — migrating from legacy monolithic systems to resilient, cloud-native, AI-augmented distributed architectures that serve millions of concurrent users. The person in this seat will be a core pillar of that transformation.

We are looking exclusively for the top 1% — engineers who think in systems, own outcomes, and lead by example.

THE OPPORTUNITY AT A GLANCE

🏗️ Architecture Ownership

Drive system design decisions across the entire backend platform. Shape the future of distributed, fault-tolerant architecture.

🤖 AI-Augmented Engineering

Embed GenAI and LLM tooling directly into the SDLC. Champion automation-first development practices across squads.

🎓 Engineering Leadership

Mentor and grow the next generation of backend engineers. Lead hiring, reviews, and cross-functional technical alignment.

KEY RESPONSIBILITIES

1. Architecture & Platform Modernisation

Lead the full migration of legacy monolithic systems to a scalable, cloud-native microservices architecture
Design and own distributed, fault-tolerant backend systems with sub-millisecond SLO targets
Architect API-first and event-driven platforms using async messaging patterns (Kafka, Pub/Sub, SQS)
Resolve systemic performance bottlenecks, concurrency conflicts, and scalability ceilings
Establish backend design standards, coding guidelines, and architectural review processes

2. Distributed Systems Engineering (Production-Grade)

Design and implement Webhook reliability frameworks with intelligent retry and exponential backoff strategies
Build idempotent, versioned APIs with enterprise-grade rate limiting and throttling controls
Implement circuit breakers, bulkheads, and resilience patterns using Resilience4j / Hystrix or equivalents
Engineer Dead-Letter Queue (DLQ) strategies and event reprocessing pipelines with guaranteed delivery semantics
Apply Saga orchestration and choreography patterns for distributed transaction integrity
Execute zero-downtime deployments and canary release strategies with rollback capability
Design and enforce multi-region disaster recovery and business continuity protocols

3. AI-Driven Engineering Practices

Champion LLM and GenAI adoption as first-class tooling across the software development lifecycle
Apply prompt engineering techniques for automated code generation, review, and documentation workflows
Utilise AI-assisted debugging, root cause analysis, and predictive performance optimisation
Build automation-first pipelines that reduce toil and accelerate delivery velocity
Evaluate and integrate emerging AI developer tools into the engineering ecosystem

4. Engineering Leadership & Culture

Own backend platforms end-to-end with full accountability across development, stability, and performance
Actively mentor, coach, and elevate engineers at all levels (L3–L6) through structured 1:1s and code reviews
Drive and lead technical hiring — from designing assessments to final hire decisions
Partner with Product, Data, DevOps, and Security stakeholders to align engineering with business objectives
Represent the engineering org in cross-functional roadmap planning and architecture decision reviews
Foster a culture of technical excellence, psychological safety, and high-velocity delivery

TECHNOLOGY STACK (HANDS-ON PROFICIENCY REQUIRED)

Languages: Java (primary) · Go · Python · Node.js · PHP · Rust

Cloud: AWS · GCP · Azure (Multi-cloud exposure preferred)

Containers: Docker · Kubernetes · Helm · Service Mesh (Istio / Linkerd)

Databases: PostgreSQL · MySQL · MongoDB · Cassandra · Redis · Elasticsearch

Messaging: Apache Kafka · RabbitMQ · AWS SQS/SNS · Google Pub/Sub

Observability: Datadog · Prometheus · Grafana · OpenTelemetry · Jaeger · ELK Stack

CI/CD & IaC: GitHub Actions · Jenkins · ArgoCD · Terraform · Ansible

AI & GenAI: OpenAI / Claude APIs · LangChain · RAG Pipelines · GitHub Copilot · Cursor

QUALIFICATIONS & CANDIDATE PROFILE

Education

B.E. / B.Tech or M.E. / M.Tech from a Tier-I or Tier-II Institution — CS, IS, ECE, AI/ML streams strongly preferred
Exceptional real-world engineering track record may be considered in lieu of institution pedigree

Experience

6 to 9 years of progressive backend engineering experience with demonstrable ownership and impact
Proven track record of shipping and scaling production SaaS / Product systems at significant user load
Exposure to and success within start-up, mid-size, and large-scale product organisations — the full spectrum
Strong computer science fundamentals: algorithms, data structures, distributed systems theory, OS internals
Demonstrated career stability — minimum 2 years average tenure per organisation
The Ideal Candidate Exemplifies
System-level thinking with an ability to hold context across code, architecture, product, and business
An ownership mindset — no task is 'not my job'; outcomes and quality are personal commitments
Strong written and verbal communication skills for asynchronous, cross-functional collaboration
Intellectual curiosity: actively follows engineering trends, contributes to the community (OSS, blogs, talks)
Bias for automation, observability, and engineering efficiency at every level
A mentor's instinct — genuine desire to grow others and raise the capability of the team around them

WHY THIS ROLE STANDS APART

✅ Transformational Scope

Lead platform modernisation at scale. Your architectural choices will define systems serving millions of users for years.

✅ AI-Forward Engineering Culture

Be at the forefront of AI-augmented development. This org invests in tools and practices that make great engineers exceptional.

✅ Established, Stable Platform

Join a company with 500–1,000 employees, proven product-market fit, and the resources to execute on a serious technical vision.

✅ Career-Defining Leadership

Operate with strategic influence, direct access to senior leadership, and a clear path toward Principal / Staff / VP Engineering.

HOW TO APPLY

This search is being managed exclusively by Recruiting Bond

Submit your application with an updated resume

Only shortlisted candidates will be contacted. All applications are treated with the strictest confidentiality.

⚡ We move fast — qualified candidates can expect a response within 48–72 business hours.

Recruiting Bond | Bengaluru, Karnataka, India | 2026

NOW HIRING · WORLD-CLASS TALENT Backend Tech Lead (Senior Level Engineering Leadership)

Placed by Recruiting Bond on behalf of a Confidential Digital Platform Leader

📍Location: Bengaluru, India (Hybrid / On-Site)

🏢Sector: Technology, Information & Media

👥Company Size: 500 – 1,000 Employees

💼Employment: Full-Time, Permanent

🎯Experience: 6 – 9 Years (Backend Engineering)

🚀 Level: Tech Lead

ABOUT THIS MANDATE

We are looking exclusively for the top 1% — engineers who think in systems, own outcomes, and lead by example.

THE OPPORTUNITY AT A GLANCE

🏗️ Architecture Ownership

Drive system design decisions across the entire backend platform. Shape the future of distributed, fault-tolerant architecture.

🤖 AI-Augmented Engineering

Embed GenAI and LLM tooling directly into the SDLC. Champion automation-first development practices across squads.

🎓 Engineering Leadership

Mentor and grow the next generation of backend engineers. Lead hiring, reviews, and cross-functional technical alignment.

KEY RESPONSIBILITIES

1. Architecture & Platform Modernisation

Lead the full migration of legacy monolithic systems to a scalable, cloud-native microservices architecture
Design and own distributed, fault-tolerant backend systems with sub-millisecond SLO targets
Architect API-first and event-driven platforms using async messaging patterns (Kafka, Pub/Sub, SQS)
Resolve systemic performance bottlenecks, concurrency conflicts, and scalability ceilings
Establish backend design standards, coding guidelines, and architectural review processes

2. Distributed Systems Engineering (Production-Grade)

Design and implement Webhook reliability frameworks with intelligent retry and exponential backoff strategies
Build idempotent, versioned APIs with enterprise-grade rate limiting and throttling controls
Implement circuit breakers, bulkheads, and resilience patterns using Resilience4j / Hystrix or equivalents
Engineer Dead-Letter Queue (DLQ) strategies and event reprocessing pipelines with guaranteed delivery semantics
Apply Saga orchestration and choreography patterns for distributed transaction integrity
Execute zero-downtime deployments and canary release strategies with rollback capability
Design and enforce multi-region disaster recovery and business continuity protocols

3. AI-Driven Engineering Practices

Champion LLM and GenAI adoption as first-class tooling across the software development lifecycle
Apply prompt engineering techniques for automated code generation, review, and documentation workflows
Utilise AI-assisted debugging, root cause analysis, and predictive performance optimisation
Build automation-first pipelines that reduce toil and accelerate delivery velocity
Evaluate and integrate emerging AI developer tools into the engineering ecosystem

4. Engineering Leadership & Culture

Own backend platforms end-to-end with full accountability across development, stability, and performance
Actively mentor, coach, and elevate engineers at all levels (L3–L6) through structured 1:1s and code reviews
Drive and lead technical hiring — from designing assessments to final hire decisions
Partner with Product, Data, DevOps, and Security stakeholders to align engineering with business objectives
Represent the engineering org in cross-functional roadmap planning and architecture decision reviews
Foster a culture of technical excellence, psychological safety, and high-velocity delivery