Gen AI Architect

About the Role:

We are looking for a highly skilled and visionary AI Architect to lead the design, development, and implementation of Generative AI solutions across AWS and Microsoft Azure environments. This role is pivotal in shaping our GenAI strategy through the creation of scalable, secure, and responsible AI systems—leveraging both agentic and non-agentic workflow designs.

You will provide technical leadership in architecting AI-powered solutions that span across retrieval-augmented generation (RAG), vector search, foundation models, prompt engineering, and enterprise-grade AI governance—all within the AWS and Azure cloud ecosystems.

Key Responsibilities:

Architect and deliver generative AI solutions in AWS (Bedrock, SageMaker) and Azure (Azure OpenAI, Azure ML) environments.

Lead the design of agentic workflows using frameworks such as AWS Agents for Bedrock or Azure orchestration tools.

Build non-agentic AI pipelines using RAG (Retrieval-Augmented Generation) methodologies with vector databases (e.g., Amazon OpenSearch, Azure Cognitive Search).

Design and implement prompt engineering and prompt management strategies for large language models (LLMs) in cloud services.

Evaluate and integrate foundation models (e.g., GPT, Claude, Titan, Phi-2, Falcon, Mistral) via Amazon Bedrock or Azure OpenAI.

Develop chunking and indexing strategies for unstructured data to support vector-based search and RAG workflows.

Ensure strong AI governance and responsible AI practices, including security, explainability, auditability, and ethical usage in alignment with enterprise policies.

Collaborate with data engineering and DevOps teams to ensure seamless data pipeline integration, model lifecycle management, and CI/CD automation.

Guide the development of reference architectures, best practices, and reusable components for GenAI use cases across business units.

Stay current with evolving GenAI capabilities in AWS and Azure ecosystems, providing technical thought leadership and strategic guidance.

Required Qualifications:

Bachelor's or Master’s degree in Computer Science, Engineering, or a related field.

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