Senior AI Engineer

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.

Company Description

Recruiting Bond International is a next-generation Talent Intelligence, Executive Search, and Human Capital Advisory firm helping start-ups, enterprises, GCCs, and VC/PE-backed companies build high-impact global teams. It is a global leader in Recruitment Process Outsourcing (RPO), executive search, and workforce consulting, specializing in building transformative talent strategies.

From high-growth startups to Fortune 500 companies, Recruiting Bond partners with organizations across 50+ industries and 140+ countries to deliver fast, scalable, and inclusive hiring solutions. The company supports businesses in scaling teams, fostering innovation, and creating talent-first strategies to achieve their goals.

With deep expertise across Technology, FinTech, Healthcare, Real Estate, and Energy, Recruiting Bond is dedicated to building careers, companies, and futures by connecting world-class talent with high-impact opportunities globally.

About the Role

Our client is hiring a Backend Engineer (India-based, Remote) to design, build, and scale the core memory infrastructure powering production-grade AI agents.

This role is intended for an experienced engineer with 7–10 years of backend engineering experience, who has deeply internalized AI-native engineering practices and actively builds using tools such as Claude Code, Codex, Cursor, Windsurf, or comparable AI development tools as a core part of their workflow.

The hiring process is intentionally non-traditional and skill-first. There is no evaluation based on IIT pedigree, LeetCode performance, or conventional resume filters. Instead, the only evaluation criterion is: how you build with AI in real-world scenarios.

Candidates are expected to submit prompt logs or transcripts from Claude Code, Codex, Cursor, or Windsurf demonstrating a feature or product they are proud of.

What You'll Own

• Build and scale backend systems powering the memory infrastructure of the product
• Own and deliver features end-to-end, integrating AI coding tools into the core development workflow
• Design, manage, and optimize database, storage, and retrieval systems for persistent memory
• Collaborate closely on system architecture, scalability, performance, and reliability engineering
• Contribute directly to product roadmap decisions based on real customer usage and production insights

Requirements

Must-Have

• 7–10 years of backend engineering experience
• Demonstrated ability to build with AI coding tools (Claude Code, Codex, Cursor, Windsurf, or comparable)
• Ability and willingness to submit prompt log transcripts from a feature or product you are proud of
• Strong Python fundamentals
• Strong PostgreSQL or comparable relational database fundamentals
• Comfort owning systems end-to-end in production
• Based in India, remote work from anywhere in the country

Nice-to-Have

• Prior AI infrastructure or developer tools product experience
• FastAPI fluency
• Open-source contributions in AI, memory, vector databases, or developer tools
• Prior experience in memory systems, RAG pipelines, or vector database engineering
• Public technical writing or conference talks on AI-native engineering practices

Job Title: AI/ML Engineer – Voice (2–3 Years)

Location: Bengaluru (On-site)

Employment Type: Full-time

About Impacto Digifin Technologies

Impacto Digifin Technologies enables enterprises to adopt digital transformation through intelligent, AI-powered solutions. Our platforms reduce manual work, improve accuracy, automate complex workflows, and ensure compliance—empowering organizations to operate with speed, clarity, and confidence.

We combine automation where it’s fastest with human oversight where it matters most. This hybrid approach ensures trust, reliability, and measurable efficiency across fintech and enterprise operations.

Role Overview

We are looking for an AI Engineer Voice with strong applied experience in machine learning, deep learning, NLP, GenAI, and full-stack voice AI systems.

This role requires someone who can design, build, deploy, and optimize end-to-end voice AI pipelines, including speech-to-text, text-to-speech, real-time streaming voice interactions, voice-enabled AI applications, and voice-to-LLM integrations.

You will work across core ML/DL systems, voice models, predictive analytics, banking-domain AI applications, and emerging AGI-aligned frameworks. The ideal candidate is an applied engineer with strong fundamentals, the ability to prototype quickly, and the maturity to contribute to R&D when needed.

This role is collaborative, cross-functional, and hands-on.

Key Responsibilities

Voice AI Engineering

• Build end-to-end voice AI systems, including STT, TTS, VAD, audio processing, and conversational voice pipelines.
• Implement real-time voice pipelines involving streaming interactions with LLMs and AI agents.
• Design and integrate voice calling workflows, bi-directional audio streaming, and voice-based user interactions.
• Develop voice-enabled applications, voice chat systems, and voice-to-AI integrations for enterprise workflows.
• Build and optimize audio preprocessing layers (noise reduction, segmentation, normalization)
• Implement voice understanding modules, speech intent extraction, and context tracking.

Machine Learning & Deep Learning

• Build, deploy, and optimize ML and DL models for prediction, classification, and automation use cases.
• Train and fine-tune neural networks for text, speech, and multimodal tasks.
• Build traditional ML systems where needed (statistical, rule-based, hybrid systems).
• Perform feature engineering, model evaluation, retraining, and continuous learning cycles.

NLP, LLMs & GenAI

• Implement NLP pipelines including tokenization, NER, intent, embeddings, and semantic classification.
• Work with LLM architectures for text + voice workflows
• Build GenAI-based workflows and integrate models into production systems.
• Implement RAG pipelines and agent-based systems for complex automation.

Fintech & Banking AI

• Work on AI-driven features related to banking, financial risk, compliance automation, fraud patterns, and customer intelligence.
• Understand fintech data structures and constraints while designing AI models.

Engineering, Deployment & Collaboration

• Deploy models on cloud or on-prem (AWS / Azure / GCP / internal infra).
• Build robust APIs and services for voice and ML-based functionalities.
• Collaborate with data engineers, backend developers, and business teams to deliver end-to-end AI solutions.
• Document systems and contribute to internal knowledge bases and R&D.

Security & Compliance

• Follow fundamental best practices for AI security, access control, and safe data handling.
• Awareness of financial compliance standards (plus, not mandatory).
• Follow internal guidelines on PII, audio data, and model privacy.

Primary Skills (Must-Have)

Core AI

• Machine Learning fundamentals
• Deep Learning architectures
• NLP pipelines and transformers
• LLM usage and integration
• GenAI development
• Voice AI (STT, TTS, VAD, real-time pipelines)
• Audio processing fundamentals
• Model building, tuning, and retraining
• RAG systems
• AI Agents (orchestration, multi-step reasoning)

Voice Engineering

• End-to-end voice application development
• Voice calling & telephony integration (framework-agnostic)
• Realtime STT ↔ LLM ↔ TTS interactive flows
• Voice chat system development
• Voice-to-AI model integration for automation

Fintech/Banking Awareness

• High-level understanding of fintech and banking AI use cases
• Data patterns in core banking analytics (advantageous)

Programming & Engineering

• Python (strong competency)
• Cloud deployment understanding (AWS/Azure/GCP)
• API development
• Data processing & pipeline creation

Secondary Skills (Good to Have)

• MLOps & CI/CD for ML systems
• Vector databases
• Prompt engineering
• Model monitoring & evaluation frameworks
• Microservices experience
• Basic UI integration understanding for voice/chat
• Research reading & benchmarking ability

Qualifications

• 2–3 years of practical experience in AI/ML/DL engineering.
• Bachelor’s/Master’s degree in CS, AI, Data Science, or related fields.
• Proven hands-on experience building ML/DL/voice pipelines.
• Experience in fintech or data-intensive domains preferred.

Soft Skills

• Clear communication and requirement understanding
• Curiosity and research mindset
• Self-driven problem solving
• Ability to collaborate cross-functionally
• Strong ownership and delivery discipline
• Ability to explain complex AI concepts simply

Experience: 5+ years production software engineering, with 2+ years working directly on LLM or agent systems in production. 

Location: Remote 

To streamline and fast-track screening, please submit your details here (if you haven’t already): https://airtable.com/appbtkr4odapnb5I6/pagqo91lKv3VJg3GT/form 

We’ll review your responses as part of the initial screening process. Please make sure you complete and submit all details through the form to be considered for the next stage. Submissions outside the form may not be considered.

Why This Role Matters

Terrabase builds agent infrastructure that enterprise customers rely on daily for SQL generation, forecasting, data analysis, and artifact delivery. Our orchestration layer routes between specialized sub-agents, manages typed handoff contracts, runs structured eval suites, and enforces correctness across every turn.

This is not a research-prototype role. You will build and evolve agent architecture, but always in service of making the system observable, typed, evaluated, recoverable, and boringly reliable in production.

What You Will Do

Own the harness architecture and middleware stack. Our LangGraph orchestrator routes between sub-agents through a layered middleware stack: file upload handling, source resolution, local context, workspace sync, state hydration, aggregation barriers, and typed handoff contracts. You will extend this stack, enforce its contracts in code, and keep it operational as routing logic and agent surfaces evolve.

Maintain typed contracts and boundaries. Agent handoffs at Terrabase carry typed contracts with barrier conditions and retry predicates. You will design these contracts, enforce them with strict typing, manage backward compatibility when contracts change, and write the contract tests that prevent silent regressions.

Own the eval suites. We run structured eval suites across routing decisions, context-resolution accuracy, multi-turn coherence, visual reference alignment, and artifact correctness. You will extend coverage, write new evals where gaps exist, and build CI gates that block releases when regressions are detected. A routing change or prompt change with no eval coverage does not ship.

Triage production failures and close the loop. When an agent turn fails in production, you will trace it in LangSmith, identify the failure class, and convert it into a durable regression test. You will own the release gates, keep prompts and runtime contracts in sync, manage feature flag rollout risk, and remove dead paths as the system evolves.

Own SQL and artifact correctness. Our agents generate SQL over customer schemas and produce structured artifacts (reports, dashboards, data sheets) under a strict schema contract. You will own the correctness layer: source grounding, schema-aware validation, provenance surfaces, and the eval infrastructure that catches generated artifact failures before they reach customers.

Build and maintain HITL workflows. Human-in-the-loop checkpoints let users intervene, redirect, or approve mid-chain. You will design these workflows, enforce their resumable state contracts, and ensure they degrade gracefully when interrupted.

Instrument for traceability. You will extend LangSmith tracing coverage, add structured span annotations, and build the tooling that lets us diagnose a bad agent turn from production trace data alone, without requiring a local reproduction.

What We Are Looking For

• 5+ years production software engineering, with strong Python fundamentals
• 2+ years working hands-on with LLM-based systems: agent loops, tool use, context management, or inference pipelines
• Experience with LangGraph, LangChain, OpenAI/Anthropic tool-use systems, or equivalent multi-step agent/runtime orchestration
• Practical eval engineering: you have built or extended eval harnesses, written automated test cases for agent behavior, and treated evaluation as an ongoing engineering discipline
• Strong engineering hygiene: strict typing, small interfaces, contract tests, clear schema migrations, and CI discipline
• Ability to debug from production traces and artifacts, not only local reproductions
• Comfort working across prompts, Python runtime code, TypeScript product surfaces, data systems, and eval infrastructure
• Systems thinking: you design for observability, recovery, and state management, not just the happy path
• Maintenance ownership mindset: you triage, close loops, and leave systems more debuggable than you found them
• Pragmatic judgment: you can distinguish between reliability-critical infrastructure and speculative abstraction

Bonus Points

• HITL workflow design: checkpoints, approvals, mid-chain interrupts, resumable state
• Context engineering depth: chunking strategies, retrieval-augmented generation, semantic routing, re-ranking
• Experience with LangSmith, Weights and Biases, or similar trace and evaluation platforms
• Prior work shipping agent systems to enterprise customers where SQL or data correctness is a hard requirement
• Experience with mypy, Pydantic contracts, or strict typing disciplines in a production Python codebase

Life at Terrabase

We are a sharp, focused, fully remote team building agent infrastructure that enterprise customers trust with their data. You will work directly alongside the engineer who designed this harness, with broad ownership, generous compute budgets, and a culture that treats reliability as a product requirement, not a research topic.

Terrabase is an equal-opportunity employer. We celebrate diversity and are committed to building an inclusive environment for every team member.

7+ years of experience in Python Development

Good experience in Microservices and APIs development.

Must have exposure to large scale data

Good to have Gen AI experience

Code versioning and collaboration. (Git)

Knowledge for Libraries for extracting data from websites.

Knowledge of SQL and NoSQL databases

Familiarity with RESTful APIs

Familiarity with Cloud (Azure /AWS) technologies

About Wissen Technology:

• The Wissen Group was founded in the year 2000. Wissen Technology, a part of Wissen Group, was established in the year 2015.

• Wissen Technology is a specialized technology company that delivers high-end consulting for organizations in the Banking & Finance, Telecom, and Healthcare domains. We help clients build world class products.

• Our workforce consists of 550+ highly skilled professionals, with leadership and senior management executives who have graduated from Ivy League Universities like Wharton, MIT, IITs, IIMs, and NITs and with rich work experience in some of the biggest companies in the world.

• Wissen Technology has grown its revenues by 400% in these five years without any external funding or investments.

• Globally present with offices US, India, UK, Australia, Mexico, and Canada.

• We offer an array of services including Application Development, Artificial Intelligence & Machine Learning, Big Data & Analytics, Visualization & Business Intelligence, Robotic Process Automation, Cloud, Mobility, Agile & DevOps, Quality Assurance & Test Automation.

• Wissen Technology has been certified as a Great Place to Work®.

• Wissen Technology has been voted as the Top 20 AI/ML vendor by CIO Insider in 2020.

• Over the years, Wissen Group has successfully delivered $650 million worth of projects for more than 20 of the Fortune 500 companies.

We have served client across sectors like Banking, Telecom, Healthcare, Manufacturing, and Energy. They include likes of Morgan Stanley, MSCI, StateStreet, Flipkart, Swiggy, Trafigura, GE to name a few.

Website : www.wissen.com

AI Engineer — Vertexcover Labs

Who We Are

Vertexcover Labs is an employee-focused, engineer-run software studio. We partner with fast-growing, funded startups around the world—Rephrase.ai, Dhiwise, Dunzo, Dubdub, Xapo, Arintra etc.—to crack their toughest engineering problems. Everyone is an individual contributor; no management layers. Engineers choose the projects they work on, see each project's P&L, and share directly in the profits.

Whether it's building multi-agent systems for production, scaling ML pipelines across GPU clusters, or shipping RAG-powered products that end-users rely on—we solve hard AI problems for real companies.

A Few Problems We Are Currently Working On

• AI Agents Test-authoring agents that convert natural language into e2e tests.
• Ad-performance agents that learn what works for your brand and generate winning creatives automatically.
• Scale + MLOps Optimise ML pipelines and fix autoscaling by applying queuing theory while juggling CPU / GPU memory contention.
• RAG & Knowledge Systems Design and deploy retrieval-augmented generation pipelines—chunking strategies, embedding models, reranking, and evaluation loops.
• AI Video & Image Processing Build rendering pipelines, diffusion-model integrations, and real-time video processing at scale. You'll own at least one project like these—design, build, iterate.

Signals You're Probably the Right Fit

• Strong in at least one other language (Python, Go, Rust, TypeScript, Java …); happy to learn more.
• First principle understanding of how LLMs, embeddings, vector databases and AI Agents work
• Evidence of shipping real AI-powered software—OSS, side projects, or production features.
• Comfortable navigating the fast-moving AI landscape—papers, new model releases, evolving APIs.
• Clear written & spoken communication; async collaboration is our default.
• Self-directed—you ask for context, not permission.

How We Operate

• Project choice. Engineers vote on which engagements we take.
• Stack agnostic. We pick tools that fit the job, not the résumé.
• Pragmatic craftsmanship. Durable design, no gold-plating.
• Transparent economics. Know what your work is worth, share in the profits.
• Remote-native. Async by default; sync when it helps.

Hiring Process (Lean & Human)

1. 2–3 technical deep-dives with future teammates.
2. 30-minute culture chat.
3. Offer. No LeetCode marathons, no trick puzzles.

We read every application and reply to all candidates.

Software Engineer

Onsite - HSR Bangalore

6 Days work from Office (Flexible working hours)

Product is a PowerPoint AI assistant used by consulting companies and Fortune 500 teams. A typical professional spends 1 to 3 hours creating one slide. With Product company, they create a v1 of their entire deck in 10 minutes, and make changes like “turn this table to a chart” in seconds directly within PowerPoint.

In the next 2 years, our goal at company is to forever change the way business presentations are made.

Who are we?

• small, strong team of 5
• founders are CS graduates from IIT Kharagpur with a specialisation in AI
• work 6 days a week from our office in HSR Layout in Bangalore
• funded by Y Combinator and other amazing investors
• used by consulting companies and Fortune 500 teams

Your responsibilities (in order)

• Design, implement, test, and deploy full features
• Design and implement a robust infrastructure to enable rapid development and automated testing
• Look at usage data to iterate on features

What we’re looking for

• Undergraduate or master's in Computer Science or equivalent degree
• 2+ years of backend or DevOps software engineering experience
• Experience with TypeScript (JavaScript) or Python

You’ll be a good fit if

• You want to work on a product that can change the way a very large number of people work
• The chaos of high growth and things breaking is exciting to you
• You are a workaholic, looking to upskill faster than most people think is possible. This role is not a good fit for you if you’re looking to prioritise work-life balance.
• You prefer working in-person with other smart people who are excited and passionate about what they’re building
• You love solving very hard problems at a rapid pace. We discuss timelines in days or weeks, so you’ll constantly be expected to ship really high-quality work.

Perks

• Comprehensive health insurance for you and dependents
• Workstation enhancements
• Subscriptions to AI tools such as Cursor, ChatGPT, etc.

(If there's anything else we can do to make your work more enjoyable, just ask)

If you are interested in proceeding, we would be happy to move your profile to the next stage of the evaluation process.

Kindly share the following details to help us take this forward :

• Current CTC (Fixed + Variable):
• Expected CTC:
• Notice Period (If currently serving, please mention your Last Working Day)
• Details of any active offers in hand (if applicable)
• Expected/Available Date of Joining (if applicable)
• Attach Updated CV:
• Attach Github Link / Leet code link or other:
• Current Location:
• Preffered Location:
• Reason for job Change:
• Reason for relocation (if applicable):
• Are you comfortable with 6 days wfo (flexible working hours)?( Yes / No):
• 
  

Company Overview 

McKinley Rice is not just a company; it's a dynamic community, the next evolutionary step in professional development. Spiritually, we're a hub where individuals and companies converge to unleash their full potential. Organizationally, we are a conglomerate composed of various entities, each contributing to the larger narrative of global excellence.

Redrob by McKinley Rice: Redefining Prospecting in the Modern Sales Era

Backed by a $40 million Series A funding from leading Korean & US VCs, Redrob is building the next frontier in global outbound sales. We’re not just another database—we’re a platform designed to eliminate the chaos of traditional prospecting. In a world where sales leaders chase meetings and deals through outdated CRMs, fragmented tools, and costly lead-gen platforms, Redrob provides a unified solution that brings everything under one roof.

Inspired by the breakthroughs of Salesforce, LinkedIn, and HubSpot, we’re creating a future where anyone, not just enterprise giants, can access real-time, high-quality data on 700 M+ decision-makers, all in just a few clicks.

At Redrob, we believe the way businesses find and engage prospects is broken. Sales teams deserve better than recycled data, clunky workflows, and opaque credit-based systems. That’s why we’ve built a seamless engine for:

• Precision prospecting
• Intent-based targeting
• Data enrichment from 16+ premium sources
• AI-driven workflows to book more meetings, faster

We’re not just streamlining outbound—we’re making it smarter, scalable, and accessible. Whether you’re an ambitious startup or a scaled SaaS company, Redrob is your growth copilot for unlocking warm conversations with the right people, globally.

EXPERIENCE

Duties you'll be entrusted with:

• Develop and execute scalable APIs and applications using the Node.js or Nest.js framework
• Writing efficient, reusable, testable, and scalable code.
• Understanding, analyzing, and implementing – Business needs, feature modification requests, and conversion into software components
• Integration of user-oriented elements into different applications, data storage solutions
• Developing – Backend components to enhance performance and receptiveness, server-side logic, and platform, statistical learning models, highly responsive web applications
• Designing and implementing – High availability and low latency applications, data protection and security features
• Performance tuning and automation of applications and enhancing the functionalities of current software systems.
• Keeping abreast with the latest technology and trends.

Expectations from you:

Basic Requirements

• Minimum qualification: Bachelor’s degree or more in Computer Science, Software Engineering, Artificial Intelligence, or a related field.
• Experience with Cloud platforms (AWS, Azure, GCP).
• Strong understanding of monitoring, logging, and observability practices.
• Experience with event-driven architectures (e.g., Kafka, RabbitMQ).
• Expertise in designing, implementing, and optimizing Elasticsearch.
• Work with modern tools including Jira, Slack, GitHub, Google Docs, etc.
• Expertise in Event driven architecture.
• Experience in Integrating Generative AI APIs.
• Working experience in high user concurrency.
• Experience in scaled databases for handling millions of records - indexing, retrieval, etc.,

Technical Skills

• Demonstrable experience in web application development with expertise in Node.js or Nest.js.
• Knowledge of database technologies and agile development methodologies.
• Experience working with databases, such as MySQL or MongoDB.
• Familiarity with web development frameworks, such as Express.js.
• Understanding of microservices architecture and DevOps principles.
• Well-versed with AWS and serverless architecture.

Soft Skills

• A quick and critical thinker with the ability to come up with a number of ideas about a topic and bring fresh and innovative ideas to the table to enhance the visual impact of our content.
• Potential to apply innovative and exciting ideas, concepts, and technologies.
• Stay up-to-date with the latest design trends, animation techniques, and software advancements.
• Multi-tasking and time-management skills, with the ability to prioritize tasks.

THRIVE

Some of the extensive benefits of being part of our team:

• We offer skill enhancement and educational reimbursement opportunities to help you further develop your expertise.
• The Member Reward Program provides an opportunity for you to earn up to INR 85,000 as an annual Performance Bonus.
• The McKinley Cares Program has a wide range of benefits:
• The wellness program covers sessions for mental wellness, and fitness and offers health insurance.
• In-house benefits have a referral bonus window and sponsored social functions.
• An Expanded Leave Basket including paid Maternity and Paternity Leaves and rejuvenation Leaves apart from the regular 20 leaves per annum. 
• Our Family Support benefits not only include maternity and paternity leaves but also extend to provide childcare benefits.
• In addition to the retention bonus, our McKinley Retention Benefits program also includes a Leave Travel Allowance program.
• We also offer an exclusive McKinley Loan Program designed to assist our employees during challenging times and alleviate financial burdens.

Must have:

• 8+ years of experience with a significant focus on developing, deploying & supporting AI solutions in production environments.
• Proven experience in building enterprise software products for B2B businesses, particularly in the supply chain domain.
• Good understanding of Generics, OOPs concepts & Design Patterns
• Solid engineering and coding skills. Ability to write high-performance production quality code in Python
• Proficiency with ML libraries and frameworks (e.g., Pandas, TensorFlow, PyTorch, scikit-learn).
• Strong expertise in time series forecasting using stat, ML, DL and foundation models
• Experience of working on processing time series data employing techniques such as decomposition, clustering, outlier detection & treatment
• Exposure to generative AI models and agent architectures on platforms such as AWS Bedrock, Crew AI, Mosaic/Databricks, Azure
• Experience of working with modern data architectures, including data lakes and data warehouses, having leveraged one or more of the frameworks such as Airbyte, Airflow, Dagster, AWS Glue, Snowflake,, DBT
• Hands-on experience with cloud platforms (e.g., AWS, Azure, GCP) and deploying ML models in cloud environments.
• Excellent problem-solving skills and the ability to work independently as well as in a collaborative team environment.
• Effective communication skills, with the ability to convey complex technical concepts to non-technical stakeholders

Good To Have:

• Experience with MLOps tools and practices for continuous integration and deployment of ML models.
• Has familiarity with deploying applications on Kubernetes
• Knowledge of supply chain management principles and challenges.
• A Master's or Ph.D. in Computer Science, Machine Learning, Data Science, or a related field is preferred

What you’ll be doing

 Weare much more than our job descriptions, but here is where you will begin:

 As a Senior Software Engineer Data & ML You’ll Be:

 ● Architect, design, test, implement, deploy, monitor and maintain end-to-end backend

 services. You build it, you own it.

 ● Work with people from other teams and departments on a day to day basis to ensure

 efficient project execution with a focus on delivering value to our members.

 ● Regularly aligning your team’s vision and roadmap with the target architecture within your

 domain and to ensure the success of complex multi domain initiatives.

 ● Integrate already trained ML and GenAI models (preferably GCP in services.

ROLE:

 Whatyou’ll need,

 Like us, you’ll be deeply committed to delivering impactful outcomes for customers.

 What Makes You a Great Fit

 ● 5 years of proven work experience as a Backend Python Engineer

 ● Understanding of software engineering fundamentals OOPS, SOLID, etc.)

 ● Hands-on experience with Python libraries like Pandas, NumPy, Scikit-learn,

 Lang chain/LLamaIndex etc.

 ● Experience with machine learning frameworks such as PyTorch or TensorFlow, Keras, being

 proficient in Python

 ● Hands-on Experience with frameworks such as Django or FastAPI or Flask

 ● Hands-on experience with MySQL, MongoDB, Redis and BigQuery (or equivalents)

 ● Extensive experience integrating with or creating REST APIs

 ● Experience with creating and maintaining CI/CD pipelines- we use GitHub Actions.

 ● Experience with event-driven architectures like Kafka, RabbitMq or equivalents.

 ● Knowledge about:

 o LLMs

 o Vector stores/databases

 o PromptEngineering

 o Embeddings and their implementations

 ● Somehands-onexperience in implementations of the above ML/AI will be preferred

 ● Experience with GCP/AWS services.

 ● You are curious about and motivated by the future trends in data, AI/ML, analytics