Key Responsibilities

GenAI & Agentic AI Architecture

- Architect end-to-end GenAI-first and Agentic AI solutions on AWS using:
- Amazon Bedrock
- Amazon AgentCore
- Strands SDK Multi-Agents
- AWS Agents for Bedrock
- Knowledge Bases for Bedrock
- Guardrails for Bedrock
- Amazon SageMaker for model lifecycle & finetuning

- Design multi-agent systems supporting planning, reasoning, tool invocation, memory stores, and multi-step workflows.
- Develop high-performance RAG architectures using:

- Amazon OpenSearch Serverless
- Kendra
- Aurora + pgvector
- Redis Enterprise

- Implement secure prompt flows, tool integrations, embeddings pipelines, and evaluation frameworks optimized for AWS.

Application & Platform Engineering

- Build Python-based microservices (FastAPI/Django) integrated with AWS AI stack.
- Design scalable architectures using AWS-native components:
- Lambda, Step Functions, EventBridge, SQS, DynamoDB, API Gateway, ECS/EKS

- Implement enterprise-class LLMOps using:

- Amazon SageMaker Pipelines
- AWS CodePipeline / CodeBuild
- CloudWatch / X-Ray observability

- Establish standards for:

- Agent governance & safety
- Secure model invocation
- Guardrails & content filters
- Latency & cost optimization patterns

Security, Reliability & Optimization

- Ensure compliance with enterprise security standards using:
- IAM, KMS, VPC endpoints, PrivateLink, CloudTrail

- Optimize AI workloads for cost, performance, and scalability, including model caching, selective batching, and autoscaling strategies.
- Conduct architecture reviews, threat modeling, and performance benchmarking.

Innovation & Framework Evangelism

- Assess new AWS AI services, foundation models, agent frameworks, and vector DBs.
- Create internal accelerators, reusable patterns, and AWS-focused reference architectures for:
- Multi-agent orchestration
- RAG 2.0 / context enrichment
- Federated retrieval
- Enterprise tool integration

- Lead PoCs, prototypes, and technical spikes to validate emerging AWS GenAI capabilities.

Technical Mentorship & Enablement

- Mentor engineering teams on AWS GenAI patterns, LLMOps, cloud-native development, and distributed AI architectures.
- Provide technical direction, best practices, and deep architectural guidance across delivery teams.
- Create internal documentation,



architecture blueprints, and engineering playbooks.

Required Skills & Qualifications

- AWS Certified Associate/Professional Solutions Architect.

Preferred Qualifications

- Expert-level experience with AWS Bedrock, AWS AgentCore, Strands SDK, AWS Agents, SageMaker, OpenSearch, Lambda, Step Functions, ECS/EKS, and DynamoDB.
- Strong Python development background (FastAPI, Django).
- Hands-on coding ability with:
- Enterprise RAG
- Agent orchestration frameworks (Strands, LangGraph, CrewAI, Bedrock Agents, etc.)
- Embedding models and vector DB design
- Exposure to data engineering, analytics, and AI-driven platforms.

- Experience in pre-sales, solutioning, and deal shaping for large digital transformation programs.
- - Key Responsibilities

GenAI & Agentic AI Architecture

- Architect end-to-end GenAI-first and Agentic AI solutions on Azure using:
- Azure OpenAI Service
- Azure AI Foundry
- Azure Agents (Agent Service + Agent Runtime)
- Microsoft Agent Framework SDK
- Azure Machine Learning (Azure ML) for model lifecycle, fine-tuning & evaluation

- Design multi-agent systems capable of planning, reasoning, memory management, tool invocation, and complex workflow execution.
- Architect enterprise-grade RAG systems using:

- Azure Cognitive Search
- Azure AI Search Vector Stores
- Cosmos DB + pgvector
- Redis Enterprise on Azure

- Build secure prompt pipelines, embeddings workflows, tool integration layers, and model evaluation frameworks optimized for Azures GenAI stack.

Application & Platform Engineering

- Build Python-based microservices (FastAPI/Django) tightly integrated with Azure AI and cloud-native components.
- Implement enterprise-grade LLMOps using:
- Azure ML Pipelines
- Azure DevOps (ADO)
- GitHub Actions for MLOps/LLMOps
- Azure Monitor, App Insights, Log Analytics for observability

- Establish engineering standards for:

- Agent governance & safety




- Content filters & guardrails (Azure OpenAI Safety Systems)
- Secure model invocation via private endpoints
- Latency reduction, performance tuning & cost governance

Security, Reliability & Optimization

- Optimize AI workloads using model caching, selective batching, adaptive routing, vector index optimization, and autoscaling strategies on AKS/Functions.
- Lead architecture reviews, threat modeling, cost modeling, and performance benchmarking for large-scale GenAI workloads.

Innovation & Framework Evangelism

- Evaluate new Azure GenAI capabilities, FMs, agent frameworks, vector databases, and orchestration tools.
- Build internal accelerators and reusable reference architectures for:
- Multi-agent orchestration with Azure Agents Framework, Langgraph, CrewAI
- RAG 2.0 and context enrichment pipelines
- Federated & hybrid retrieval patterns
- Enterprise system/tool integration

- Drive PoCs, prototypes, and tech spikes to validate emerging Azure GenAI capabilities and accelerate enterprise adoption.

Technical Mentorship & Enablement

- Mentor engineering teams on Azure GenAI engineering, LLMOps, cloud-native patterns, and distributed AI architectures.
- Provide deep technical guidance, best practices, and architectural oversight across delivery projects.
- Develop internal documentation, architecture blueprints, engineering guides, and reusable templates.

Required Skills & Qualifications

- Microsoft Certified Azure Solutions Architect Expert or equivalent Azure Architect certification.
- Solid hands-on experience designing AI/ML or GenAI systems on Azure.

Preferred Qualifications

- Expert-level experience with:
- Azure OpenAI, Azure AI Foundry, Azure Agents Framework, Langgraph, CrewAI, Azure ML
- Cognitive Search, Cosmos DB, AKS, Functions, APIM, Event Grid, Service Bus

- Strong Python development background (FastAPI, Django).
- Hands-on coding experience in:

- RAG systems
- Agent orchestration frameworks (Azure Agents, LangGraph, CrewAI, etc.)
- Embedding models, vector indexing & vector DB architecture

- Exposure to data engineering, analytics, and cloud-scale distributed systems.
- Experience supporting pre-sales, architecture assessments, and solutioning for GenAI transformation programs.

📌 Gen AI Architect (Pune)
🏢 Naukri E-hire Campaign
📍 Pune