Lead AI Engineer Responsibilities Drive technical vision and architecture by leading the design and implementation of highly scalable, robust enterprise AI applications, including complex multi-agent workflows, custom search engines, and end-to-end AI systems Partner closely with clients and business stakeholders to translate business goals into technical roadmaps and recommend high-impact, feasible LLM-driven solutions Establish engineering standards by setting the benchmark for code quality, architectural patterns, robust data pipelines, smart prompt management, and automated evaluation frameworks Foster a culture of learning and high performance through design reviews, development best practices, and active mentorship of junior and senior developers Direct R&D and innovation by guiding research on emerging models, framing prototyping strategies (PoCs), and standardizing the tools and frameworks used to stay on the bleeding edge of Generative AI Requirements 5+ years of experience in a Lead, Principal, or Senior AI/Software Engineering role, with a track record of architecting and shipping complex, production-grade AI or ML-based solutions At least 1 year of relevant leadership experience Master-level proficiency in Python (specifically FastAPI or equivalent web frameworks) and a strong foundation in core NLP concepts (semantic similarity, tokenization, NER, text classification) Expertise in modern Generative AI design patterns, particularly RAG (Retrieval-Augmented Generation), autonomous multi-agent networks, and tool integration Production experience with major LLM APIs (OpenAI, Anthropic, Bedrock, Gemini) and framework orchestration tools such as LangGraph or LlamaIndex Background in optimizing AI applications for performance, security (prompt injection mitigation, data privacy), cloud costs, and system evaluation using programmatic or LLM-as-a-judge approaches Capability to quickly direct and build intuitive UI/UX prototypes using Streamlit, Gradio, or equivalent Outstanding leadership and communication skills to lead engineering discussions, bridge the gap between technical and non-technical audiences, and present technical architectures to executives and clients Strong English communication skills (B2 level or higher) Nice to have Full-stack capabilities with TypeScript Experience incorporating AI into the SDLC to boost team efficiency using tools like GitHub Copilot, Cursor, or specialized agents for automation Cloud architecture expertise in AWS or Azure (specifically Azure OpenAI, AWS Bedrock, or cloud-native containerized deployments) Familiarity with Databricks (MLflow, Agent Bricks, Unity Catalog) for orchestrating data pipelines and model lifecycles Knowledge of advanced search/retrieval systems (hybrid search, vector databases, custom rerankers and ranking algorithms) Familiarity with emerging open-source protocols (like Model Context Protocol - MCP) and LLM monitoring tools (like LangSmith or Arize Phoenix)