Senior AI Engineer (Remote)

Key ResponsibilitiesBuild agent-based systems for data product creation, pipeline automation, and operations.Develop AI copilots for data modeling, pipeline authoring, governance, and RCA.Implement multi-step reasoning, tool/function calling, and workflow planning.Integrate AI with platforms like Airflow, dbt, Fabric, and Airbyte.Create high-quality prompts, structured outputs, guardrails, and templates.Build retrieval pipelines, vector indexes, and hybrid RAG systems.Deploy, monitor, and optimize models in production.Develop evaluators, reliability checks, and secure inference flows.Collaborate across platform, DevOps, and UX teams.Support product architecture and roadmap decisions.Technical RequirementsEngineering & Backend6+ years software engineering; strong Python.Experience shipping production backend services.AI / LLM2–3+ years hands-on LLM experience; shipped real AI features.Strong prompt engineering and familiarity with OpenAI/Anthropic or OSS models.Experience fine-tuning, LoRA/PEFT, or similar approaches.Experience building agents, multi-step reasoning, and tool-calling (LangChain, LlamaIndex, DSPy, Semantic Kernel).RAG & RetrievalExperience with embeddings, vector search, and at least one RAG pipeline.Hands-on with Azure Cosmos DB (vector indexing).Bonus: Azure Cognitive Search.Data & Platform ContextUnderstanding of ETL/ELT, DAGs, dbt, and general data engineering.Comfortable integrating AI with real system APIs.Deployment & DevOpsExperience deploying to Azure (AKS, Functions, App Service, Container Apps).Strong monitoring, guardrails, and performance awareness for AI systems.Solid Azure experience: Entra ID, Managed Identity, Key Vault, Storage, Cosmos DB.CI/CD with GitHub Actions or Azure DevOps.TraitsHigh ownership, fast execution, and comfort with ambiguity.Strong communication, architecture reasoning, and debugging skills.Product-first mindset.