Technical Lead
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Job Description: Tech Lead – AI
Role: Tech Lead (AI Automation)
Experience: 4+ 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
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