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 .