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.
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• 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.
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• 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.
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• 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.
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• 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.
• Collaborate with client stakeholders, architects and engineering teams to translate business needs into working GenAI solutions. Mandatory Skills Description:
• Strong proficiency in Python,



with proven experience designing, developing 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 from implementation and deployment to monitoring and observability.
• Solid hands-on experience across the Generative AI ecosystem, including developing applications that consume and integrate with LLMs such as OpenAI GPT and Anthropic Claude.
• Hands-on experience with Microsoft Azure.
• Familiarity with Azure services such as AKS, Cosmos DB and Azure Cache for Redis is a strong plus.
• Strong hands-on experience with AI-assisted development tools and IDEs, such as Claude, Codex and VS Code.
• Positive understanding of DevOps and containerization practices, including experience with GitHub Actions, Docker and Kubernetes / AKS.
• Upper-Intermediate (B2) or higher English, with the ability to communicate directly with US-based stakeholders. Nice-to-Have Skills Description:
• 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.

📌 Senior GenAI Engineer (India)
🏢 Luxoft
📍 India