AI Engineering Tech Lead

We are looking for AI Engineering Tech Lead to drive the design and delivery of AI agent systems and multi-agent architectures. This is a technical leadership role combining deep hands-on engineering with technical leadership — guiding architectural decisions, mentoring engineers, and maintaining high standards across the codebase. 5+ years of experience in software engineering with a strong focus on AI/ML systems Expert-level Python skills, including async programming and design patterns. Demonstrated experience building AI agents and multi-agent systems using LangChain and LangGraph. Strong practical knowledge of LLM integration patterns: prompt engineering, function/tool calling, retrieval-augmented generation (RAG), embeddings, and vector search. Extensive experience with cloud platforms - AWS and/or Azure - including deployment, scaling, and management of AI workloads. Solid general ML foundation: understanding of model training, evaluation, inference pipelines, and the broader ML development lifecycle. Strong CI/CD pipeline expertise. Hands-on experience with containerization and orchestration in production environments. Practical experience with infrastructure-as-code tools for managing cloud resources reliably and repeatably. Experience implementing AI observability. Proficiency in using AI tools for everyday tasks (Claude Code, Cursor, Advanced prompting, etc) Experience designing and building robust APIs (FastAPI, Flask, or similar) and integrating them into larger system architectures. Proficiency with SQL and NoSQL databases. Ability to lead technical discussions, conduct meaningful code reviews, and mentor team members. Upper-Intermediate English or higher. Would be and advantage: High knowledge of core ML frameworks Hands-on experience with AWS SageMaker and broader AWS ML ecosystem. Solid understanding of the full ML lifecycle. Responsibilities: Lead the technical design and architecture of AI agent platforms and multi-agent workflows built on LangChain and LangGraph. Hands-on development of AI agents. Integrate LLMs from providers such as OpenAI, Anthropic, and Azure OpenAI into production-grade agent pipelines. Build and optimize CI/CD, containerization, and infrastructure-as-code practices for the team. Establish and maintain AI observability across agent systems - tracing execution paths, monitoring performance, tracking costs, and surfacing anomalies. Mentor and guide engineers through code reviews, architectural discussions, and knowledge sharing sessions. Collaborate with product managers, solution architects, and stakeholders to align technical implementation with business objectives. Ensure system reliability, scalability, and maintainability through clean architecture, automated testing, and deployment best practices. Contribute to defining engineering standards, development workflows, and documentation practices across the team. Contribute to technical solutions for AI-oriented proposals during pre-sale cycles