Senior Technical Lead / Solution Archite...

BMW TechWorks India Private Limited; Pune District; Hybrid; INR 3,000,000 - 6,000,000; Full time

Job summary

BMW TechWorks India Private Limited in Pune is seeking a Tech Lead to drive AI automation for automotive IVI test frameworks. You will lead a growing engineering team, contribute hands-on coding, and shape the AI-driven test strategy.

You will design multi-agent workflows, review architecture, integrate tools, and mentor engineers while delivering reliable, scalable automation across the platform.

Qualifications

• Strong Python production-grade experience with tests and CI/CD pipelines.
• Architect scalable, maintainable software systems.
• Experience integrating APIs and 3rd-party services; collaborative with external teams.

Responsibilities

• Coordinate and technically guide a team of engineers working on the framework.
• Own and improve the overall system architecture (agent orchestration, tool integration, extension).
• Design, build and review new agents and capabilities within the framework.
• Diagnose and fix bugs across the agent pipeline.
• Introduce and maintain observability for agentic workflows (tracing, tool calls, token handling, failure modes).
• Build and maintain evaluation pipelines.

Skills

Python

Software architecture

APIs & async

LLM integration

CI/CD

Distributed systems

Education

Bachelor's degree in engineering

Tools

LangChain

LangGraph

Semantic Kernel

AutoGen/AG2

CrewAI

Job description

AI Automation and AI CI/CD Integration Python AI Tools

B.Tech/B.E

Job Description

Job Description: Tech Lead – AI

Role: Tech Lead (AI Automation)

Experience: 8+ Years

Role Overview

We are seeking a visionary Tech Lead to drive our next-generation Automotive Infotainment (IVI) systems test automation framework using high capability AI driven approach. You will also spearhead our AI-driven test automation strategy, integrating LLMs and Agentic AI workflows to design automation frameworks.

This is a hands-on hybrid role requiring individual coding contribution, framework design review, cross-functional stakeholder management, and technical mentorship for a growing engineering team.

Responsibilities

• Coordinate and technically guide a team of engineers working on the framework
• Own and improve the overall system architecture (agent orchestration, tool integration, extension)
• Design, build and review new agents and capabilities within the framework
• Diagnose and fix bugs across the agent pipeline
• Introduce and maintain observability for agentic workflows (tracing agent, tool calls, token, failure modes)
• Build and maintain evaluation pipelines

Required skills

• Strong professional Python experience (production grade code, tests, CI/CD, packaging)
• Solid software architecture skills, be able to design maintainable and modular systems
• Comfortable to work with APIs, async code, integrate 3rd party services and tools and collaborate with various external teams
• Practical experience building multi-step or multi-agent systems with a framework such as LangChain, LangGraph, Semantic Kernel, AutoGen/AG2, CrewAI, or an equivalent custom orchestration layer
• Solid understanding of tool/function calling, structured output, and how to design reliable agent-to-tool interfaces
• Experience with prompt engineering: versioning prompts, testing them systematically, understanding failure modes (hallucination, tool misuse, context loss) and mitigating them
• Working knowledge of context management techniques (chunking, RAG, memory strategies) and their trade-offs — not necessarily building embeddings/vector infra from scratch, but knowing when and how to use it
• Experience setting up or using LLM observability tooling (e.g. LangSmith, Langfuse, OpenTelemetry-based tracing, Arize, or in-house equivalents) to debug agent runs in production
• Experience designing or running evaluation frameworks for LLM/agent output quality (e.g. LLM-as-judge, golden datasets, regression suites, automated scoring)
• Awareness of cost/latency trade-offs between models and how to design systems that use the right model for the right step
• Comfortable reading model provider docs/API changes (OpenAI, Azure OpenAI, Anthropic, etc.) and adapting the system accordingly