Recruiting Bond

Director of Engineering — Flights Platform

AI-First Travel Commerce · High-Scale Distributed Systems · Marketplace Infrastructure

🌏 The Problem Space

A flight search looks trivially simple. It is anything but.

Every query you fire triggers a choreography of distributed systems operating in real-time — integrating with a dozen airline GDS/NDC providers, computing dynamic fares across inventory buckets and fare rules, ranking thousands of itineraries by relevance and business intent, and returning a ranked, priced, bookable result set — all in under 100ms.

→ Millions of search queries per minute

→ <100ms end-to-end SLA with external API dependencies

→ High-value transactions — a bug here means a missed booking, not a failed render

→ Pricing errors erode trust faster than any other failure mode

We are rebuilding the Flights platform as a real-time commerce engine for Bharat — AI-native from day zero, built to power both B2C consumer journeys and high-stakes B2B enterprise corridors.

This is a once-in-a-decade opportunity to build national-scale flight infrastructure from first principles.

🧠 What You Will Own

You will own the full Flights platform — systems, architecture, and the teams that build them.

Core System Domains:

•Search Systems — high-throughput, low-latency query pipelines returning ranked, bookable options

•Pricing & Fare Engine — dynamic pricing logic, fare rules, promotional overlays, and real-time validation

•Booking & Ticketing — transaction-critical flows requiring strict consistency, idempotency, and zero data loss

•Airline Integrations — managing unreliable external GDS/NDC APIs with retries, circuit-breakers, and reconciliation

•Post-Booking Flows — cancellations, modifications, refunds — correctness at the margin is non-negotiable

Platform Scope:

•High-scale APIs serving consumer apps, B2B enterprise clients, and third-party partners

•Event-driven state machines managing booking workflows across async boundaries

•Observability and reliability infrastructure across all mission-critical flows

Team Scope:

•Lead 15–30+ engineers across multiple product and platform teams

•Manage Engineering Managers and Principal/Staff engineers

•Own hiring, org design, and technical direction

⚙️ Core Engineering Challenges

This role is fundamentally about making the right trade-offs under uncertainty — at scale.

Latency vs. Accuracy — when do you serve a cached fare vs. call a live airline API?

Availability vs. Consistency — graceful degradation at booking time vs. strict price validation

Cost vs. Performance — when is an external API call worth it vs. a cache hit?

Scalability vs. Simplicity — the best system is the one your team can reason about under incident

🤖 AI-First Engineering

AI is not an afterthought. It is load-bearing architecture.

•LLM-powered pricing intelligence — dynamic fare prediction and demand signals

•RAG pipelines for fare rules, refund policy, and support automation

•Agentic booking resolution workflows — autonomous exception handling at scale

•MCP-based orchestration layers for multi-provider integration

⚖️ Key Responsibilities

Architecture & Distributed Systems

•Design and evolve sub-100ms distributed query systems serving millions of concurrent searches

•Build fault-tolerant booking pipelines with strong consistency and durability guarantees

•Drive Kafka-based event architectures for booking state management

Reliability & Observability

•Own 99.99%+ availability for booking and pricing systems

•Build deep observability — metrics, distributed tracing, structured logging, SLOs/SLAs

•Lead post-incident reviews and drive systemic reliability improvements

Business Partnership

•Partner with Product, Revenue, and Partnerships to translate commercial goals into architecture

•Influence platform roadmap, supplier strategy, and long-term technical investment

🛠️ Technology Stack

Backend: Java · Kotlin · Go · Python

Architecture: Microservices · Event-Driven (Kafka) · gRPC

Data: Redis · Aerospike · DynamoDB · Elasticsearch

Cloud: AWS (EKS, EC2, S3)

Observability: Prometheus · Grafana · OpenTelemetry

👤 Who You Are

•12–16 years in backend/distributed systems; 5+ Years in an Engineering Leadership role, led teams of 15–50 engineers

•Built and scaled large B2C + B2B platforms — Travel Tech, FinTech, or high-scale Consumer

•Deep expertise in real-time systems, marketplace dynamics, and external API integration

•Tier-I institute background strongly preferred (IIT / IIIT / NIT / IISC / BITS / VIT / SRM — CSE/ISE)

🚀 Why This Matters

Build national-scale infrastructure for 1.4 billion people

Sit at the intersection of AI · distributed systems · marketplace economics

Define the future of travel commerce in India — from architecture to product

🤖 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)

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.

🚨 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

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.

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

Who we are: My AI Client is building the foundational platform for the "agentic economy," moving beyond simple chatbots to create an ecosystem for autonomous AI agents and they aim to provide tools for developers to launch, manage, and monetize AI agents as "digital coworkers."

The Challenge

The current AI stack is fragmented, leading to issues with multimodal data, silent webhook failures, unpredictable token usage, and nascent agent-to-agent collaboration. My AI Client is building a unified, robust backend to resolve these issues for the developer community.

Your Mission

As a foundational member of the backend team, you will architect core systems, focusing on:

• Agent Nervous System: Designing agent-to-agent messaging, lifecycle management, and high-concurrency, low-latency communication.
• Multimodal Chaos Taming: Engineering systems to process and understand real-time images, audio, video, and text.
• Bulletproof Systems: Developing secure, observable webhook systems with robust billing, metering, and real-time payment pipelines.

What You'll Bring

• My AI Client seeks an experienced engineer comfortable with complex systems and ambiguity.

Core Experience:

● Typically 3 to 5 years of experience in backend engineering roles.

● Expertise in Python, especially with async frameworks like FastAPI.

● Strong command of Docker and cloud deployment (AWS, Cloud Run, or similar).

● Proven experience designing and building microservice or agent-based architectures.

Specialized Experience (Ideal):

• Real-Time Systems: Experience with real-time media transmission like WebRTC, WebSockets and ways to process them.
• Scalable Systems: Experience in building scalable, fault-tolerant systems with a strong understanding of observability, monitoring, and alerting best practices.
• Reliable Webhooks: Knowledge of scalable webhook infrastructure with retry logic, backoffs, and security.
• Data Processing: Experience with multimodal data (e.g., OCR, audio transcription, video chunking with FFmpeg/OpenCV).
• Payments & Metering: Familiarity with usage-based billing systems or token-based ledgers.

Your Impact

• The systems designed by this role will form the foundation for:
• Thousands of AI agents for major partners across chat, video, and APIs.
• A new creator economy enabling developers to earn revenue through agents.
• The overall speed, security, and scalability of my client’s AI platform.

Why Join Us?

• Opportunity to solve hard problems with clean, scalable code.
• Small, fast-paced team with high ownership and zero micromanagement.
• Belief in platform engineering as a craft and care for developer experience.
• Conviction that AI agents are the future, and a desire to build their powering platform.
• Dynamic, collaborative in-office work environment in Bengaluru in a Hybrid setup (weekly 2 days from office)
• Meaningful equity in a growing, well-backed company.
• Direct work with founders and engineers from top AI companies.
• A real voice in architectural and product decisions.
• Opportunity to solve cutting-edge problems with no legacy code.

Ready to Build the Future?

My AI Client is building the core platform for the next software paradigm. Interested candidates are encouraged to apply with their GitHub, resume, or anything that showcases their thinking.