AI Engineer

Responsibilities

• Design and deploy end-to-end agentic AI workflows using planner–worker, orchestrator–executor, multi-agent, and RAG-based architectures
• Build reusable components and reference patterns for tool routing, state management, error handling, and human-in-the-loop checkpoints
• Implement robust retrieval pipelines (hybrid search, vector + keyword, graph-aware retrieval) over technical documents, historian data, and operational records
• Translate domain problems from drilling, completions, production, geophysics, and power plant operations into well-scoped agentic use cases with clear success metrics
• Build and maintain a semantic layer over our data lake and warehouse using Snowflake Semantic Views and Databricks Unity Catalog Metric Views, making business concepts queryable by both humans and agents
• Develop and curate knowledge graphs that connect domain entities (wells, pads, assets, equipment, events, documents) and serve as grounding context for LLM reasoning
• Standardize how agents access enterprise data through Model Context Protocol (MCP) servers and equivalent integration patterns
• Establish agent evaluation frameworks including golden datasets, automated regression tests, and structured evals for accuracy, faithfulness, and tool-use correctness
• Implement tracing, logging, and observability across agent runs to support debugging, cost monitoring, and continuous improvement
• Build feedback loops that capture user input and convert it into eval cases and prompt/system improvements
• Support production incidents and platform-level issues impacting deployed agents
• Deploy agents as production services on our Azure-native stack (App Service, Container Apps, Functions) with Entra , Key Vault-managed secrets, and proper cost controls
• Build lightweight UIs (Streamlit, Gradio, or React) for agentic applications and internal tools
• Lead design reviews and cross-functional enablement sessions on agentic AI patterns and best practices

• Bachelor's or Master's degree in Computer Engineering or Data Science preferred
• 2+ years of hands-on experience building and deploying agentic AI or LLM-powered applications in production, not just prototypes or notebooks
• Strong Python skills, including async patterns, API design with FastAPI, and writing testable, maintainable production code
• Demonstrated experience with at least one major agent framework:
  Lang Chain/Lang Graph, Llama Index, Auto Gen, or Semantic Kernel
• Working knowledge of LLM APIs and SDKs (Anthropic Claude, OpenAI, Azure OpenAI), including tool use/function calling, structured outputs, streaming, and prompt engineering
• Experience implementing RAG architectures with vector databases (Azure AI Search, pgvector, Pinecone, Weaviate, Chroma, or similar) and embedding models
• Experience with agent orchestration patterns including multi-step planning, tool routing, state management, and graceful failure handling
• Familiarity with Model Context Protocol (MCP) or equivalent standards for tool and context integration
• Cloud deployment experience on Azure (App Service, Container Apps, Functions, Key Vault, Entra ), or equivalent in AWS/GCP with willingness to work in our Azure-first environment
• Strong Git and CI/CD experience, including version control discipline, code review, and automated testing
• Experience with containerization (Docker) and infrastructure-as-code (Terraform preferred)
• Strong observability and production operations skills, including structured logging, tracing, cost monitoring, and runbook development