Senior AI Engineer

Role summary Implements AI and Forward Deployed Engineering capabilities across client-facing feature suites, combining hands-on GenAI engineering with solution deployment, integration, configuration, and field feedback loops. The role focuses on building and adapting RAG, GraphRAG, agentic workflows, orchestration integrations, and data-connected AI features so they work reliably in real enterprise environments. Key responsibilities Develop and configure AI-enabled features across assigned client-facing capability suites Implement RAG, GraphRAG, prompt workflows, agent routing, and retrieval pipeline integrations Work directly with product, business, data, and engineering stakeholders to translate field requirements into deployable technical solutions Build dynamic query-generation and graph-aware retrieval capabilities for knowledge-graph-backed use cases Integrate AI capabilities with backend APIs, orchestration services, vector stores, enterprise data sources, and platform services Validate outputs against golden datasets, acceptance criteria, grounding rules, and enterprise quality expectations Support field deployment, configuration, troubleshooting, demos, UAT feedback, and production-readiness activities Document solution behavior, deployment assumptions, known limitations, and upgrade paths for future releases Required skills & experience 5+ years hands-on AI, ML, data, backend, or solution engineering experience, with strong Python fundamentals Practical experience building GenAI / LLM applications using RAG, GraphRAG, prompt engineering, embeddings, and vector stores Experience integrating AI features with enterprise systems, APIs, data pipelines, orchestration layers, or workflow platforms Comfortable working in a Forward Deployed Engineering model, including client-facing discovery, rapid prototyping, field configuration, issue triage, and stakeholder demos Familiarity with agent frameworks such as LangGraph, LangChain, MCP-based integrations, or comparable orchestration patterns Ability to validate AI outputs using golden datasets, regression checks, grounding criteria, and measurable acceptance criteria Strong communication skills with the ability to explain technical trade-offs, implementation constraints, risks, and field observations to technical and business stakeholders Preferred qualifications Experience delivering AI solutions in regulated, enterprise, healthcare, pharma, manufacturing, or data-governed environments