Architect

About the Role – AI Architect (Generative AI)

We are seeking a visionary and technically strong AI Architect to lead the design, development, and deployment of Generative AI solutions across AWS and Azure environments. This is a critical role for shaping our GenAI strategy across global enterprise customers.

Key Responsibilities :

Architect and implement GenAI solutions on AWS (Bedrock, SageMaker) and Azure (Azure OpenAI, Azure ML).

Design both agentic and non-agentic workflows using tools like LangChain , Semantic Kernel , or AWS Agent Framework .

Develop RAG (Retrieval-Augmented Generation) pipelines using vector databases (e.g., Amazon OpenSearch, Azure Cognitive Search).

Build and manage prompt engineering strategies and prompt lifecycle.

Evaluate and integrate leading foundation models (e.g., GPT , Claude , Titan , Phi-2 , Falcon , Mistral ).

Implement chunking / indexing strategies for unstructured data to support RAG and vector-based retrieval.

Ensure responsible AI practices, including governance, security, explainability, and compliance.

Collaborate with data engineering and DevOps teams for pipeline integration, model lifecycle, and CI / CD automation.

Develop reference architectures and best practices for reusable GenAI components.

Stay up to date with AWS / Azure GenAI innovations and provide strategic guidance.

Required Qualifications :

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

Proven ability to design and deploy AI workflows on :

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

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

Strong experience in RAG , prompt engineering , and vector database design .

Familiar with AI agent orchestration frameworks (LangChain, Semantic Kernel, AWS Agent Framework).

Solid understanding of cloud security , IAM / RBAC , and compliance in enterprise settings.

Proficiency in Python and modern ML libraries / APIs across AWS and Azure ecosystems.

Preferred Qualifications :

Experience with LLMOps tools : model monitoring, logging, performance tracking.

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

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

Ability to convert business needs into scalable, measurable AI solutions .