Team Lead - Agentic Systems

Overview

We are seeking a seasoned Team Lead – Agentic Systems to guide and mentor an engineering team in building, scaling, and productionizing enterprise-grade autonomous AI applications, AI Agents, and AI Automations. In this role, you will combine hands-on technical expertise in LLM orchestration, RAG pipelines, and agentic workflows with team leadership—driving best practices, system reliability, and architectural standards across multi-agent enterprise deployments. You will translate business vision into production AI systems while fostering team growth and continuous technical improvement.

Work Mode: Hybrid

Work Location: Bangalore

Experience: 7-9 yrs

Key Responsibilities

• Lead, mentor, and grow a team of AI and software engineers; perform regular code reviews, manage sprint execution, and drive career development and hiring.
• Own the end-to-end architecture, delivery, and operational reliability of production-grade, autonomous AI agents, multi-agent teams, and workflow automations.
• Work with ARB to establish standards and design patterns for LLM orchestration (LangChain, LangGraph, etc.), tool/API integration, state management, and multi-step reasoning systems.
• Define and implement continuous evaluation harnesses and production observability.
• Implement enterprise-grade guardrails, access control boundaries, prompt injection defense, and governance across multi-tool, multi-agent systems.
• Lead the engineering of advanced Retrieval-Augmented Generation (RAG) pipelines, integrating vector databases, hybrid search, and structured/unstructured enterprise data stores.
• Build and maintain agent integrations with enterprise applications across multiple business use cases, via REST APIs, connector frameworks, or MCP-style tool registries.
• Operate within existing access-control, logging, and audit patterns for multi-tool agent systems; flag gaps to the architecture team rather than redesigning independently
• Collaborate closely with Product, Security, Enterprise Architecture, and Infrastructure teams to align agentic capabilities with strategic business use cases.

Required Skills (Must Have)

• 7 years of professional software engineering experience, including at least 1–2 years of hands-on building with LLMs or agentic frameworks.
• 1+ years of experience in direct team leadership, technical mentorship, or managing small-to-medium-sized engineering teams.
• Strong proficiency in Python and/or Node.js for AI and backend development.
• Working knowledge of major LLM providers (e.g., OpenAI, Anthropic) and prompt orchestration frameworks (LangChain, LangGraph).
• Hands-on expertise in building LLM applications, including
• Prompt engineering and enforcing structured outputs
• Tool integration and function calling
• Retrieval-Augmented Generation (RAG) using vector databases and embedding-based search
• Designing multi-agent workflows and orchestration frameworks
• Managing context windows and conversational state
• Experience integrating external tools and APIs via function calling or tool-registration mechanisms.
• Experience integrating agents or tools with enterprise SaaS applications via REST APIs or connector frameworks.
• Working knowledge of SQL and NoSQL databases and how they plug into AI pipelines.
• Experience writing test cases or evaluation harnesses for agent/LLM behavior (accuracy, hallucination, tool-call correctness).
• Familiarity with agent evaluation techniques, including LLM-as-a-judge, task-based evaluations, benchmark design, and automated regression testing.
• Understanding of access-control and audit-logging patterns in multi-tool, multi-agent systems.
• Understanding of AI security, prompt injection, data leakage, permission boundaries, and agentic-system guardrails.
• Strong debugging skills and the ability to implement complex multi-step systems within an established architecture.

Nice to Have Skills

• Understanding of cloud deployment and infrastructure (AWS, GCP).
• Knowledge of containerization and CI/CD pipelines (Docker, GitHub Actions).
• Experience with AI observability, guardrails, and safety mechanisms for LLMs.
• Familiarity with data warehouses or analytical query engines for large-scale structured retrieval.
• Background in enterprise SaaS integration patterns and multi-tenant system design.