8+ years of experience in software/data architecture with 3+ years in AI/ML, including hands-on work with generative AI solutions.

Proven experience designing and deploying AI workflows using:

AWS: Amazon Bedrock, SageMaker, Lambda, DynamoDB, OpenSearch.

Azure: Azure OpenAI, Azure ML, Azure Cognitive Services, Cognitive Search.

Expertise in RAG pipeline architecture, prompt engineering, and vector database design.

Familiarity with tools and frameworks for AI agent orchestration (e.g., LangChain, Semantic Kernel, AWS Agent Framework).

Strong understanding of cloud security, identity management (IAM, RBAC), and compliance in enterprise environments.

Proficiency in Python and hands-on experience with modern ML libraries and APIs used in AWS and Azure.

Preferred Qualifications:

Experience working with LLMOps tools in cloud environments (e.g., model monitoring, logging, performance tracking).

Understanding of fine-tuning strategies, model evaluation, and safety/risk management of GenAI models.

Familiarity with serverless architecture, containerization (ECS, AKS), and CI/CD practices in AWS/Azure.

Ability to translate business problems into scalable AI solutions with measurable outcomes.