Sr Specialist Software Engineering (AI Forward Deployed Engineer)

Experience 8+ years of relevant software engineering, cloud engineering, AI/ML engineering, platform engineering, or solution engineering experience, including hands-on delivery of production-grade Generative AI, RAG, or Agentic AI systems Key Responsibilities Work directly with business and technical stakeholders to identify high-value AI use cases and convert ambiguous requirements into production-ready solutions. Design and build Generative AI, Retrieval-Augmented Generation (RAG), Advanced RAG, and Agentic AI applications. Engineer retrieval pipelines covering document ingestion, parsing, chunking, metadata enrichment, embeddings, indexing, hybrid retrieval, reranking, grounding, and response generation. Build single-agent and multi-agent systems with planning, reasoning, tool calling, workflow orchestration, state management, memory, retries, exception handling, and human-in-the-loop approvals. Develop and integrate Model Context Protocol (MCP) servers and clients to securely connect AI agents with enterprise tools, APIs, data sources, and workflows. Build reusable tools, functions, plugins, APIs, and connectors for structured and controlled agent execution. Develop AI-enabled backend services and microservices using Python, FastAPI, Flask, REST APIs, asynchronous processing, and event-driven patterns. Integrate AI solutions with enterprise applications, databases, SaaS platforms, cloud services, security platforms, and knowledge repositories. Apply prompt engineering and context engineering, including system prompts, few-shot prompting, structured outputs, context compression, and conversation-memory strategies. Build automated evaluation pipelines to measure retrieval quality, groundedness, faithfulness, answer relevance, hallucination, task completion, latency, security, token consumption, and cost. Implement LLMOps and GenAIOps practices covering model and prompt versioning, testing, deployment, tracing, observability, rollback, and production monitoring. Implement AI security guardrails against prompt injection, indirect prompt injection, jailbreaks, sensitive-data disclosure, insecure tool execution, privilege escalation, and excessive agency. Rapidly develop proofs of concept and convert validated prototypes into secure, scalable, resilient, maintainable, and supportable enterprise applications. Troubleshoot complex AI, retrieval, application, data, API, cloud, and integration issues and drive them through root-cause analysis and closure. Create technical designs, architecture diagrams, evaluation reports, runbooks, deployment documentation, and operational support procedures. Collaborate with Product Owners, Architects, Software Engineers, DevOps, Cybersecurity, Data Engineering, and business teams while maintaining clear delivery visibility. Required Technical Skills Must Have Strong programming experience in Python, including production-grade backend and AI application development.