Project description
We are seeking a hands-on GenAI Engineer to join a team developing production-grade, AI-powered solutions for a major US insurance provider.
Designed and developed an enterprise-grade Generative AI platform using Python, FastAPI, and FastMCP to automate recruitment workflows and provide intelligent candidate insights. Built scalable, production-ready APIs that integrated with OpenAI GPT and Anthropic Claude models for candidate screening, resume analysis, job matching, and automated recruiter assistance.
Implemented RAG (Retrieval-Augmented Generation) pipelines using vector databases such as Pinecone/Milvus to provide context-aware responses from enterprise knowledge repositories. Developed AI Agents and custom plugins/skills to automate candidate engagement, interview scheduling, talent recommendations, and workflow orchestration.
Owned the complete application lifecycle, including architecture design, API development, deployment, monitoring, observability, logging, and performance optimization. Leveraged AI-assisted development tools such as Claude, Codex, and VS Code to accelerate development, debugging, code reviews, and delivery.
Responsibilities
- Design and deliver production-grade GenAI solutions using OpenAI GPT and Anthropic Claude models.
Build agentic workflows using LLM orchestration frameworks such as LangChain, LangGraph or similar, including multi-step reasoning, tool use and human-in-the-loop patterns.
Design and implement RAG pipelines covering document ingestion, chunking, embeddings, hybrid search, re-ranking and retrieval quality tuning.
Develop MCP servers using FastMCP to expose enterprise tools and data to AI agents.
Create and extend Skills and Plugins that adapt LLM capabilities to insurance-specific workflows.
Work with vector databases to support semantic search and knowledge retrieval.
Implement LLM evaluation, guardrails and safety controls, including hallucination detection, prompt injection protection and PII handling.
Implement observability and diagnostics for LLM systems: tracing, evaluations, token and cost tracking, latency and quality monitoring.
Build scalable backend APIs in Python and FastAPI that serve GenAI capabilities to client applications.
Own the full lifecycle of GenAI applications: prototyping, development, deployment, monitoring and continuous improvement.
Use AI-assisted development tools (Claude, Codex) as a core part of the daily engineering workflow.
Advise client stakeholders on GenAI feasibility, solution design, cost and model selection.
SKILLS
Must have
- GenAI experience (primary focus):
2+ years of hands-on GenAI engineering experience, with at least one LLM-based solution delivered to production.
Deep practical experience integrating OpenAI GPT and Anthropic Claude: prompt engineering, tool/function calling, structured outputs, streaming and context management.
Proven experience designing, building and tuning RAG pipelines in real-world projects.
Hands-on experience building agentic solutions with LangChain, LangGraph, LlamaIndex or similar frameworks.
Practical experience with MCP servers (FastMCP preferred) and LLM extension mechanisms such as Skills and Plugins.
Hands-on experience with at least one vector database (e.g., Pinecone, Weaviate, Qdrant, Chroma, pgvector, Azure AI Search).
Experience with LLM observability and evaluation tooling (e.g., LangSmith, Langfuse, Arize Phoenix, OpenTelemetry).
Understanding of LLM limitations and risks: hallucinations, prompt injection, cost and latency trade-offs, and model selection.
Engineering:
4+ years of professional software development experience with a strong focus on Python.
Solid experience building production REST APIs with FastAPI.
Experience deploying and running applications in production: Docker, CI/CD and at least one major cloud platform (AWS, Azure or GCP).
Daily, confident use of VS Code and AI coding assistants such as Claude and Codex.
Upper-Intermediate (B2) or higher English, with the ability to communicate directly with US-based stakeholders.
Nice to have
Experience applying GenAI in insurance or financial services (e.g., claims automation, document understanding, underwriting assistance).
Experience with fine-tuning, embeddings optimization or open-source LLMs.
Experience with multi-agent architectures and agent-to-agent communication.
Kubernetes and infrastructure-as-code (Terraform, Bicep).
Knowledge of responsible AI practices and compliance requirements for PII in regulated industries.