MetLife

Job Responsibilities

·       Architect and deliver AI-driven solutions that address high‑value business needs, including document interpretation, automated decision flows, feedback generation, dashboard creation, data reconciliation, and approval management.

·       Lead the development of agent-based workflows using LLMs, retrieval pipelines, and multi‑agent orchestration frameworks.

·       Build Composite AI architectures, combining language models, search, business rules, embeddings, and analytics.

·       Proven experience designing, developing, and deploying Generative AI (GenAI) solutions using large language models (LLMs) such as GPT, Llama, Claude, etc

·       Design and optimize context pipelines—chunking strategies, prompting structure, memory systems, and vector retrieval mechanisms.

·       Develop robust backend components and API integrations supporting AI agents, Azure AI services, and MCP-based tools.

·       Experience with modern GenAI frameworks and libraries (e.g., LangChain, LlamaIndex, Hugging Face Transformers).

·       Evaluate new use cases, propose viable AI solutions, and guide stakeholders through feasibility and solution design.

·       Ensure solutions align with responsible AI standards, quality benchmarks, and enterprise governance requirements.

·       Collaborate with architects, product managers, and business teams to align AI initiatives with enterprise goals.

·       Monitor system performance, optimize agent behaviors, and evolve solutions post-deployment.

·       Extensive experience with Microsoft Azure services and cloud architecture patterns

·       Deep understanding of CI/CD pipelines, automated testing, and DevOps practices

·       Experience with microservices architecture, API design, and distributed systems

·       Experience mentoring engineers and building high-performing teams

.

Knowledge, Skills and Abilities

Education

·       Bachelor’s degree or master’s in computer science, Engineering, or related technical discipline.

Experience

·       3–5+ years of AI engineering experience, ideally within large organizations or enterprise platforms.

·       Proven track record of leading AI or GenAI initiatives, from concept to deployment.

Knowledge and skills (general and technical)

Strong command of:

·       Large Language Models and modern GenAI techniques

·       Agentic design principles (tool‑augmented agents, planners, multi-agent coordination)

·       Retrieval-based systems (embeddings, vector search, context assembly)

·       Proficiency in Python and experience developing scalable backend components and APIs.

·       Hands-on expertise with Azure AI / Azure OpenAI, Azure Functions, APIM, storage, and related cloud services.

·       Experience building solutions that go beyond simple automation tools—focusing instead on intelligent, adaptive systems.

·       Ability to collaborate directly with business partners, understand real workflows, and translate them into AI-powered solutions.

Other Requirements (licenses, certifications, specialized training – if required)

Experience with advanced Agentic frameworks:

·       LangChain, LangGraph, Azure AI Agents

·       Multi-agent routing, tool-calling patterns, DAG-based orchestration

·       Hands-on experience with vector databases (Azure AI Search, Pinecone, FAISS).

·       Exposure to MCP (Model Context Protocol) and enterprise tool-chains integrating LLM agents with backend systems.

·       Prior involvement in building solutions for domains such as:

·       Operational decision flows

·       Document-heavy processes

·       KPI/insights generation

·       Approval or workflow automation

·       Familiarity with cloud-native architecture, DevOps pipelines, and observability tooling.

·       Experience with AI developer tools: GitHub Copilot, OpenAI’s code assistants, or similar systems.

·       Understanding of enterprise data protection, compliance considerations, and responsible AI practices.