Design develop and deploy enterprise grade Generative AI applications using modern AI frameworks and cloud platforms
Build and optimize Retrieval Augmented Generation RAG solutions leveraging vector databases and enterprise knowledge repositories
Develop AI agents and multiagent workflows using LangChain LangGraph Semantic Kernel and AutoGen frameworks
Integrate Large Language Models LLMs such as GPT Llama Claude and Gemini into business applications
Collaborate with architects product owners and business stakeholders to deliver AIdriven solutions aligned with business objectives
Implement prompt engineering context management memory frameworks and orchestration workflows to improve AI performance
Develop scalable APIs microservices and backend services using Python and cloudnative technologies
Finetune evaluate and monitor foundation models to enhance accuracy reliability and user experience
Implement LLMOps and MLOps practices including CICD pipelines monitoring observability and model lifecycle management
Ensure compliance with Responsible AI principles security standards data privacy requirements and governance policies
Optimize AI applications for scalability performance latency and cost efficiency in production environments
Troubleshoot complex AI solution issues and provide technical leadership during development and production support
Mentor junior engineers and contribute to technical design reviews coding standards and best practices
Stay updated with advancements in GenAI Agentic AI multimodal AI and emerging AI technologies
Required Skills 7 years in software engineering with strong expertise in Python GenAI Azure OpenAI Azure AI Foundry RAG Vector Databases LangChain LangGraph REST APIs Docker Kubernetes Git and cloud platforms
Skills
Mandatory Skills : Generative AI Evaluation
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