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Niche GenAI/FDE skillset and lesser-known employer reduce qualified applicant density.
Specialized GenAI and enterprise integration experience moderately limits cross-industry transferability.
Explicit 5+ years plus specific GenAI, Python, and integration requirements enforce strict shortlisting.
Build and deploy AI-enabled features such as RAG, GraphRAG, prompt workflows, and agent routing within client-facing products.
Integrate AI capabilities with backend APIs, orchestration services, and enterprise data sources ensuring reliability in enterprise environments.
Support field activities including troubleshooting, demos, UAT feedback, production readiness, and documentation of solution behavior and limitations.
5+ years of hands-on experience in AI, ML, data, backend, or solution engineering with strong Python skills.
Proven experience developing GenAI/LLM applications using RAG, GraphRAG, prompt engineering, embeddings, and vector stores.
Experience in integrating AI features with enterprise systems, APIs, data pipelines, orchestration layers, or workflow platforms.
Work Experience Required: 5+ years; Notice Period: Not explicitly mentioned in the JD.
Experienced in Forward Deployed Engineering including client-facing discovery, rapid prototyping, configuration, and stakeholder communication.
Familiarity with agent frameworks like LangGraph, LangChain, MCP-based integrations, or similar orchestration patterns.
Ability to validate AI outputs against golden datasets, grounding criteria, and measurable acceptance criteria, indicating a detail-oriented and quality-focused approach.