Browse current jobs

Senior GenAI Engineer (Azure)

Luxoft · Remote

Apply on employer website

Collected role description

Role overview

Senior GenAI Engineer (Azure) at Luxoft

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.

Responsibilities

  • Build agentic workflows that combine LLM reasoning, tool use and multi-step task execution.
  • Build scalable, production-grade APIs and end-to-end solutions covering the full lifecycle: implementation, deployment, monitoring and observability.
  • 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.

Required qualifications

  • Skills Must have Strong proficiency in Python, with proven experience designing, developing and implementing applications using FastAPI and FastMCP.

Preferred qualifications and skills

  • Nice to have Experience applying GenAI in insurance or financial services (e.g., claims automation, document understanding, underwriting assistance).

Employment details

  • Location: Remote India [India]

Application details

  • Designed and developed an enterprise-grade Generative AI platform using Python, FastAPI, and FastMCP to automate recruitment workflows and provide intelligent candidate insights.
  • Developed AI Agents and custom plugins/skills to automate candidate engagement, interview scheduling, talent recommendations, and workflow orchestration.
  • 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.

Additional role details

  • 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.
  • 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.
  • 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.
  • Deploy and operate services on Microsoft Azure, including AKS, Cosmos DB and Azure Cache for Redis.
  • 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.
  • 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.
  • Good 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.
  • 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.
  • Other Languages English: B2 Upper Intermediate Seniority Senior