Technical AI Lead

Job Title: Technical AI Lead

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Job Summary

Technical AI Lead

Agentic AI, autonomous agents, and Large Language Models (LLMs)

Python

Key Responsibilities

• Design, build, and optimize agentic systems and multi-agent workflows for real-world use cases.
• Develop and maintain tailored LLM pipelines using frameworks such as LangChain, LangGraph, and related ecosystems.
• Deploy, serve, and optimize open-source LLMs using vLLM, TGI (Text Generation Inference), and Python-based inference stacks.
• Work extensively with open models such as Qwen, Llama, Mistral, and other leading model families, including fine-tuning, evaluation, and integration.
• Implement scalable AI services with robust prompt engineering, autonomous task planning, and tool execution.
• Define AI architecture, performance benchmarks, and best practices in collaboration with cross-functional teams.
• Provide technical leadership, mentor team members, and ensure high engineering standards across deliverables.
  

Required Skills & Experience

• 6+ years of strong hands-on experience in Python development.
• Proven expertise in Agentic AI, agent frameworks, and autonomous orchestration systems.
• Hands-on experience building custom LLM pipelines and multi-agent systems.
• Solid experience with vLLM, TGI, and model serving infrastructure.
• Extensive exposure to open-source LLMs such as Qwen, Llama, Mistral, and similar models.
• Strong working knowledge of LangChain, LangGraph, and related agent frameworks.
• Deep understanding of inference optimization, embeddings, vector databases, and model integration patterns.
• Excellent communication skills with the ability to clearly articulate ideas to leadership and peers.
• Strong delegation, influence, and decision-making capabilities.
  

Preferred / Nice-to-Have Skills

• Experience designing multi-agent architectures for analytics, automation, or workflow systems.
• Ability to build custom tools, connectors, or model-controller interfaces (e.g., MCP).
• Hands-on experience with RAG pipelines, LLM evaluation frameworks, and scalable inference environments.
• Prior experience leading or owning AI platforms or agent-based systems end-to-end.