AI AWS Technical Architect

Job Title: AI AWS Technical Architect
Duration- Fulltime Permanent
Location: Parsippany, NJ (Onsite from Day 1)
Job Description:
Must Have Technical/Functional Skills
• 10+ years of experience in Software Development & ML engineering, including 4+ years in Solution architecture or
AI architecture delivering production-grade systems.
• Strong hands-on expertise in Python (workflow orchestration, model evaluation), Node.js, and experience working
with agent-based frameworks such as AutoGen, LangChain, or ADK. Solid knowledge of prompt engineering techniques
and tool-calling mechanisms.
• Deep understanding of retrieval-augmented systems, including embeddings, chunking strategies, ranking mechanisms,
and Vector databases such as Qdrant and MongoDB Atlas with vector search capabilities.
• Extensive experience in MLOps practices, including CI/CD pipelines for ML models, model registry management,
and containerized deployments using Kubernetes.
• Strong cloud expertise in AWS, with hands-on experience in services such as Amazon Bedrock, SageMaker,
AWS Lambda, and Amazon EKS.
• Demonstrated experience in implementing Responsible AI practices, security best practices
(encryption, secrets management, network isolation), and FinOps strategies for AI workloads, including cost
and latency optimization.

Roles & Responsibilities
• Architect and design an end-to-end solution for an AI-driven Retrieval-Augmented system.
• Define and implement the embedding strategy, document chunking methodology, and ranking techniques
to ensure high-quality retrieval.
• Evaluate, select, and integrate appropriate Large Language Models (LLMs) using Amazon Bedrock.
• Design and implement a scalable vector database solution (such as OpenSearch, Qdrant, or MongoDB Atlas
with vector search).
• Architect and deploy the cloud infrastructure leveraging AWS services including Amazon Bedrock (LLM), SageMaker,
AWS Lambda, and Amazon EKS.
• Establish MLOps best practices by designing CI/CD pipelines for model deployment and managing model registry
updates.
• Apply FinOps principles to balance cost and performance, optimizing model size, inference latency, and
caching strategies.
• Design secure access controls and implement secrets management using AWS Secrets Manager.