Senior GenAI Engineer
Luxoft is hiring a Senior GenAI Engineer to build production-grade, AI-powered solutions for a major US insurance provider. Day to day: developing agentic LLM applications and APIs in Python (FastAPI, FastMCP) with OpenAI/Anthropic models, deployed on Azure (AKS, Cosmos DB, Redis) with full CI/CD, monitoring and observability.
- Compensation
- Not disclosed
- Location
- Not listed
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- Not specified
- Category
- Ai engineering
- Posted
Skills
Must have
- Strong proficiency in Python with proven experience designing and implementing applications using FastAPI and FastMCP
- Hands-on experience building agentic workflows and LLM-driven applications delivered to production
- Ability to build scalable, production-grade APIs and end-to-end solutions covering the complete development lifecycle (implementation, deployment, monitoring, observability)
- Solid hands-on experience across the Generative AI ecosystem, integrating LLMs such as OpenAI GPT and Anthropic Claude
- Hands-on experience with Microsoft Azure
- Strong hands-on experience with AI-assisted development tools and IDEs such as Claude, Codex and VS Code
- Good understanding of DevOps and containerization, including GitHub Actions, Docker and Kubernetes/AKS
- Upper-Intermediate (B2) or higher English, with ability to communicate directly with US-based stakeholders
Nice to have
- Familiarity with Azure services such as AKS, Cosmos DB and Azure Cache for Redis
- Experience applying GenAI in insurance or financial services (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
Full description
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.
Core Technology Stack
Backend: Python, FastAPI, FastMCP
GenAI / LLMs: OpenAI GPT, Anthropic Claude, LLM Integrations, GenAI Ecosystem, Agentic Workflows
DevOps: GitHub Actions, Docker, Kubernetes
Cloud: Microsoft Azure, AKS, Cosmos DB, Azure Cache for Redis
Development Tools: VS Code, Claude, Codex
Engineering Focus: Scalable APIs, End-to-End Application Development, Deployment, Monitoring, Observability
Responsibilities
- Design, develop and implement GenAI applications in Python using FastAPI and FastMCP.
Build agentic workflows that combine LLM reasoning, tool use and multi-step task execution.
Develop MCP servers with FastMCP to securely expose enterprise data, systems and tools to AI agents.
Integrate and consume LLMs such as OpenAI GPT and Anthropic Claude, including prompt engineering, tool/function calling, structured outputs and streaming.
Build scalable, production-grade APIs and end-to-end solutions covering the full lifecycle: implementation, deployment, monitoring and observability.
Deploy and operate services on Microsoft Azure, including AKS, Cosmos DB and Azure Cache for Redis.
Build and maintain CI/CD pipelines with GitHub Actions and containerize applications with Docker.
Implement monitoring and observability for both services and LLM behavior: tracing of LLM and tool calls, token and cost tracking, latency and quality metrics.Use AI-assisted development tools (Claude, Codex, VS Code) as a core part of the daily engineering workflow.