AI AWS Technical Architect

Job Title: AI AWS Technical Architect
Duration- Fulltime Permanent
Location: Parsippany, NJ (Onsite from Day1)
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.