Senior AI Engineer (Remote)

Key Responsibilities

• Build 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 Requirements

Engineering & Backend

• 6+ years software engineering; strong Python.
• Experience shipping production backend services.

AI / LLM

• 23+ 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 & Retrieval

• Experience with embeddings, vector search, and at least one RAG pipeline.
• Hands-on with Azure Cosmos DB (vector indexing).
• Bonus: Azure Cognitive Search.

Data & Platform Context

• Understanding of ETL/ELT, DAGs, dbt, and general data engineering.
• Comfortable integrating AI with real system APIs.

Deployment & DevOps

• Experience 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.

Traits

• High ownership, fast execution, and comfort with ambiguity.
• Strong communication, architecture reasoning, and debugging skills.
• Product-first mindset.