Looking for a highly skilled GenAI and Agentic AI Architect with deep hands-on expertise across AWS AI/ML services, multi-agent systems, LLM orchestration, and cloud-native engineering.

This role centers on architecting and delivering scalable, secure, production-grade GenAI solutions using AWS AgentCore, AWS Bedrock, AWS Strands SDK, vector databases, and Python-based agentic AI solutions.

You will define reference architectures, code solutions, lead complex implementations, and drive adoption of advanced GenAI and agentic patterns across enterprise systems.

Key Responsibilities :

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

GenAI & Agentic AI Architecture :

- Architect end-to-end GenAI-first and Agentic AI solutions on AWS using :

1. Amazon Bedrock

2. Amazon AgentCore

3. Strands SDK Multi-Agents

4. AWS Agents for Bedrock

5. Knowledge Bases for Bedrock

6. Guardrails for Bedrock

7. Amazon SageMaker for model lifecycle & fine-tuning

- Design multi-agent systems supporting planning, reasoning, tool invocation, memory stores, and multi-step workflows.

- Develop high-performance RAG architectures using :

1. Amazon OpenSearch Serverless

2. Kendra

3. Aurora + pgvector

4. 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 :

1. Lambda, Step Functions, EventBridge, SQS, DynamoDB, API Gateway, ECS/EKS

- Implement enterprise-class LLMOps using :

1. Amazon SageMaker Pipelines

2. AWS CodePipeline / CodeBuild

3. CloudWatch / X-Ray observability

- Establish standards for :

1. Agent governance & safety

2. Secure model invocation

3. Guardrails & content filters

4. Latency & cost optimization patterns

Security, Reliability & Optimization :

- Ensure compliance with enterprise security standards using :

1. 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 :

1. Multi-agent orchestration

2. RAG 2.0 / context enrichment

3. Federated retrieval

4. 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 :

1. Enterprise RAG

2. Agent orchestration frameworks (Strands, LangGraph, CrewAI, Bedrock Agents, etc.)

3. Embedding models and vector DB design

4. Exposure to data engineering, analytics, and AI-driven platforms.