AI Engineer

Role purpose: Design, build, and productionize Generative AI and data-driven solutions that improve business workflows, decision-making, and customer experiences. Core responsibilities - Develop and deploy GenAI applications (e.g., RAG, agents, copilots) using LLMs and vector search. - Build reliable data pipelines and feature/embedding workflows for training and inference. - Design evaluation frameworks for quality, safety, latency, and cost; run experiments and A/B tests. - Implement MLOps/LLMOps practices: CI/CD, model/version management, monitoring, and incident response. - Integrate solutions with APIs and enterprise systems; ensure scalability and security. - Collaborate with product, engineering, and stakeholders to translate requirements into technical deliverables. - Document architectures, data flows, and operational runbooks. Required skills - Programming: Python; strong software engineering fundamentals (testing, code reviews, design patterns). - GenAI: Prompting, RAG, embeddings, vector databases, tool/function calling, agent frameworks. - Data: SQL, data modeling, ETL/ELT, data quality, analytics fundamentals. - ML/AI: Model evaluation, metrics, experimentation, responsible AI and privacy considerations. - Cloud & DevOps: Containers, orchestration, observability; experience with major cloud platforms. Success outcomes - Deployed GenAI systems that are measurable, secure, and cost-efficient. - High-quality outputs with documented evaluation and monitoring. - Maintainable, well-documented servic...