The Mid-Senior Applied AI Engineer is responsible for designing,developing, and deploying scalable artificial intelligence solutions within an enterprise AIplatform. Moving beyond basic implementation, this role requires architectural thinkingto build robust GenAI services, complex retrieval pipelines (RAG), and agenticworkflows. The engineer will partner closely with AI Program Management, Data &Analytics, and business stakeholders to translate validated business requirements intosecure, production-ready AI applications.

Key Responsibilities Platform Development :
Design and build core AI application componentssupporting enterprise AI initiatives, including agentic workflows, batchprocessing, and data integrations.
Advanced Retrieval & Agentic Architectures: Implement and optimizesophisticated embeddings, vector search strategies, and multi-agent workflows tohandle both text and structured data retrieval from core enterprise systems. API & Backend Engineering: Develop secure, high-performance backend APIs (primarily FastAPI/Python) to facilitate seamless integration between foundationalmodels and internal enterprise architecture.Model Integration & Orchestration: Work with multiple LLMs (OpenAI, Claude,Gemini) via model-agnostic routing layers, optimizing for cost, latency, and task- specific performance. Feasibility & Scoping: Collaborate directly with AI Program Managers and Business Analysts during the intake phase to assess technical feasibility,architecture requirements and data readiness for new business requests. Deployment & LLMOps: Drive the deployment of AI systems, establishing CI/CD pipelines, containerization, and robust LLM monitoring (observability, prompt drift, and accuracy metrics). Governance & Compliance: Ensure all AI components strictly adhere toenterprise AI governance, security, and data privacy standards. Mentorship: Provide technical guidance and code reviews for junior developers on the team. Required Technology Stack AI Models: Deep familiarity with API integration and prompting for OpenAI,Anthropic (Claude), and Google (Gemini) models. Frameworks: Advanced proficiency in LangChain, LangGraph, and LlamaIndexfor building RAG pipelines and agentic decisioning systems. Backend & APIs: Strong expertise in Python and FastAPI. Basic working knowledge of Node.js. Data Architecture