Connecting Chains
Website:
connectingchains.com
Job details:
Experience - 10 years +
Location - Gurugram
Role Overview
We are looking for an Engineering Manager to lead a team building agentic AI and GenAI systems for enterprise clients. This is a player-coach role: you will manage engineers and their delivery, while staying hands-on enough to review architecture, unblock hard technical problems, and hold the line on quality and craft. You will own team output, technical direction, and delivery predictability for your pod.
Key Responsibilities
Manage and grow a team of AI/ML and software engineers building agentic AI and GenAI systems in production.
Own technical direction for the team: architecture choices, design patterns, and code quality for agent workflows, RAG pipelines, and LLM integrations.
Plan and track delivery: break down roadmaps into sprints, manage timelines, and remove blockers to keep the team predictable and on schedule.
Stay hands-on: review code and designs, pair on hard problems, and step in on critical issues when needed.
Own team hiring, onboarding, performance management, and career growth for engineers on the team. Partner with architects, product managers, and delivery leads to translate client requirements into engineering plans.
Own engineering quality bar: testing, CI/CD, code review standards, and production reliability for AI systems. Drive adoption of evaluation, guardrails, and observability practices for agentic AI and LLM-based systems.
Represent the team's technical progress and risks to senior leadership and, where needed, to clients.
Required Qualifications
10+ years of hands-on software engineering experience, with a strong, unbroken technical track record.
Demonstrated experience managing or leading engineering teams, including hiring, performance management, and career development.
Hands-on experience building agentic AI systems, including at least one major agent framework (LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Bedrock Agents/Strands, or Semantic Kernel).
Practical experience with GenAI/LLM systems: RAG pipelines, prompt engineering, structured outputs, and tool calling across providers.
Strong Python skills (TypeScript/Node.js a plus), with production-grade testing, CI/CD, and API design practices.
Experience managing delivery: sprint planning, estimation, and predictable execution against timelines.
Cloud-native experience on AWS and/or Azure: containers, serverless, event backbones, and vector databases.
Understanding of LLM safety and reliability practices: guardrails, prompt-injection defences, evaluation, and observability.
Preferred Qualifications
Experience managing distributed or hybrid engineering teams. Experience integrating enterprise systems such as Salesforce, Microsoft Dynamics, ServiceNow, or SAP.
Experience with fine-tuning, model distillation, or self-hosted/open-weight model deployment. Experience working with stakeholders from the Middle East, ideally government or semi-government entities.
Certifications:
AWS ML Specialty, AWS Solutions Architect Professional, or Azure Solutions Architect Expert.
What We Offer Ownership:
Real authority over technical direction and delivery for your team.
AWS Advanced Tier ecosystem: direct access to AWS specialist teams and competency pathways.
Global exposure: clients across 20+ countries and regular engagement with senior leadership. Career trajectory: a credible path to Director of Engineering or Head of AI Engineering.
Click on Apply to know more.
