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