About the Role :

We are looking for an experienced AI Platform Engineer for our US InsurTech Client to build the Agentic Orchestration Layer powering next-generation intelligent insurance platforms. This is a hands-on engineering role focused on building enterprise AI agents, multi-agent orchestration workflows, RAG pipelines, context and memory services, and AI tool integrations.

You will work on production-grade AI systems integrated with enterprise applications and Guidewire services. If you have strong hands-on experience building and deploying LLM-powered applications, Agentic AI systems, and enterprise RAG solutions, we would like to hear from you.

Roles & Responsibilities :

- Design and build multi-agent AI systems and orchestration workflows.

- Develop enterprise-grade RAG pipelines and context/memory services.

- Build AI tools, function calling, tool calling, and agent integrations.

- Integrate AI applications with enterprise APIs and Guidewire services.

- Create and maintain prompt libraries, evaluation frameworks, and AI guardrails.

- Optimize AI systems for latency, accuracy, reliability, and inference cost.

- Collaborate with Platform Engineering teams for production deployment and scalability.

Required Skills & Experience :

- Strong programming experience in Python.

- Hands-on experience with FastAPI, REST APIs, and asynchronous programming.

Strong experience with one or more AI orchestration frameworks such as :

1. LangGraph

2. LangChain

3. Semantic Kernel

4. CrewAI / AutoGen

- Strong understanding and hands-on experience with :

1. Large Language Models (LLMs)

2. Retrieval-Augmented Generation (RAG)

3. Embeddings

4. Vector databases

- Experience with vector technologies such as Pinecone, OpenSearch, or FAISS.

- Hands-on exposure to Amazon Bedrock, AWS Lambda, API Gateway, and Step Functions.

- Experience in prompt engineering, tool calling, function calling, and structured outputs.

- Working knowledge of Git and Docker.

Nice to Have :

- Guidewire experience.

- Insurance domain knowledge.

- Knowledge Graphs / GraphRAG experience.

- AI Security and Governance experience.

What We Are Looking For :

- We are particularly interested in engineers who have built real-world, production-grade GenAI applications.

The ideal candidate should be able to demonstrate hands-on experience in areas such as :

- Building end-to-end RAG systems.

- Designing stateful or multi-agent workflows.

- Integrating LLMs with enterprise APIs and external tools.

- Implementing context and memory management.

- Deploying and optimizing AI applications for production environments.

- Improving AI application accuracy, latency, reliability, and cost efficiency.

Interested candidates who can join within 30 days or less are encouraged to apply.