Gen AI Engineer

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Role Overview

MS Azure Engineer

Large Language Models (LLMs) and generative AI solutions

implementing, deploying, and monitoring LLM-based AI solutions

prompt engineering, agentic AI frameworks, retrieval-augmented generation (RAG), and AI service integration

Key Responsibilities

• LLM Implementation & Deployment
• Implement and deploy LLM-based AI solutions for enterprise use cases such as FinOps, CostOps, and other business-critical applications.
• Validate, monitor, and optimize AI model performance and relevance in production environments.
• Prompt Engineering & Optimization
• Design and implement role-based, context-aware, and instruction-tuned prompts for AI agents.
• Optimize prompts for accuracy, relevance, and performance of generative AI outputs.
• Programming & Integration
• Develop and maintain Python applications using LangChain, MCP, OpenAI SDK, and related AI libraries.
• Integrate LLM and AI services into enterprise applications via REST APIs and SDKs.
• Retrieval-Augmented Generation (RAG)
• Build data ingestion pipelines to enhance model responses using enterprise data.
• Implement semantic search using vector databases and embedding models to support RAG frameworks.
• AI Agent Experience
• Work with agentic AI frameworks (e.g., LangGraph) to design autonomous AI agents.
• Implement workflows and pipelines for multi-agent AI solutions.
• Monitoring & Performance Management
• Continuously monitor deployed LLM solutions for performance, reliability, and cost optimization.
• Troubleshoot and resolve production issues efficiently.
  

Required Skills & Qualifications

• Hands-on experience with generative AI models such as OpenAI, LLaMA, Claude, etc.
• Strong understanding of prompt engineering (role-based, context-aware, instruction-tuned).
• Expertise in Python programming, LangChain, MCP, and OpenAI SDK.
• Experience in RAG (Retrieval-Augmented Generation), vector databases, and embedding models.
• Familiarity with agentic AI frameworks such as LangGraph.
• Experience integrating AI solutions into enterprise applications via APIs and SDKs.
• Knowledge of Azure AI services and cloud deployment best practices.
  

Preferred Skills

• Exposure to multi-agent AI workflows and autonomous agent orchestration.
• Hands-on experience in enterprise-scale AI deployment and monitoring.
• Understanding of AI safety, governance, and ethical considerations.
  

Soft Skills

• Strong analytical and problem-solving skills.
• Excellent communication and collaboration abilities.
• Ability to work in dynamic, fast-paced environments with flexible priorities.
• Commitment to continuous learning and staying up-to-date with emerging AI technologies.