Technical Lead (AI Expert) - AIGent

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