Role Summary :

We are seeking a highly experienced Agentic AI Solution Architect to design and scale autonomous AI systems on AWS. This role will architect multi-agent frameworks, LLM-powered decision systems, and enterprise-grade AI platforms that integrate with data warehouses, MarTech ecosystems, and business applications. The architect will define technical strategy for agent orchestration, reasoning pipelines, retrieval-augmented generation (RAG), and AI governance while ensuring security, scalability, and cost efficiency.

Key Responsibilities :

- Architect multi-agent systems using planner - executor - critic patterns and autonomous workflows.

- Design and deploy foundation models using Amazon Bedrock, SageMaker, and open-source LLMs.

- Build scalable RAG pipelines with vector databases and knowledge bases.

- Lead AWS architecture across VPC, IAM, KMS, S3, Glue, Redshift, ECS/EKS, and Step Functions.

- Develop event-driven AI automation frameworks using Lambda and EventBridge.

- Establish AI governance including explainability, bias monitoring, hallucination detection, and audit logging.

- Implement infrastructure as code using Terraform, CloudFormation, or AWS CDK.

- Integrate AI agents with enterprise data warehouses, MarTech systems, CRM platforms, and APIs.

- Define observability and telemetry standards for agent performance and reliability.

- Translate business use cases into scalable AI solution architectures.

Required Qualifications :

- 8+ years of experience in cloud architecture.

- 3+ years designing AI/ML systems on AWS.

- Hands-on experience with Amazon Bedrock, SageMaker, and vector databases.

- Strong Python programming skills and experience building distributed systems.

- Experience with LangChain, LlamaIndex, AutoGen, or similar agentic frameworks.

- Deep understanding of RAG architecture and semantic metadata models.

Preferred Qualifications :

- AWS Certified Solutions Architect - Professional.

- Experience deploying production-grade agentic systems.

- Background in regulated industries such as Pharma, Healthcare, or Financial Services.

- Experience implementing AI governance and compliance frameworks.

Technical Stack :

- Foundation Models: Amazon Bedrock, OpenAI APIs, HuggingFace

- Orchestration: AWS Step Functions, Lambda, EventBridge

- Storage: S3, Redshift, DynamoDB

- Vector Databases: OpenSearch, Pinecone

- Data Processing: Glue, EMR

- Deployment: ECS, EKS

- Observability: CloudWatch, OpenTelemetry

Leadership & Strategic Responsibilities :

- Define enterprise AI reference architecture and roadmap.

- Mentor engineers and ML teams on best practices for agentic AI design.

- Lead build vs buy decisions for AI platforms and tools.

- Establish AI cost governance and FinOps framework.

- Drive cross-functional alignment between business, data, and engineering teams.

Success Metrics (First 6 - 12 Months) :

- Production deployment of at least one enterprise-grade multi-agent system.

- Documented enterprise AI reference architecture and governance model.

- Operational RAG and evaluation framework with measurable performance benchmarks.

- Scaled AI use cases across multiple business domains.

- Optimized AI cost-to-value ratio with clear ROI metrics.