Job Description
Role Overview Hands-on AI Architect to design and deliver enterprise-grade AI systems spanning Agentic AI, AWS Bedrock, and the full AI/ML spectrum. You will architect multi-agent orchestration pipelines, drive FM and model strategy across cloud and open-source ecosystems, embed AI across enterprise data and application layers, and serve as the senior technical voice in C-suite engagements.
Key Responsibilities Agent Core Design:
Architect the foundational agent loop — perception, planning, tool selection, execution, self-reflection, and memory — using frameworks such as LangGraph, AutoGen, CrewAI, or custom Bedrock-native implementations. Design for goal decomposition, error recovery, and dynamic replanning in long-horizon autonomous tasks. Agentic Architecture on Bedrock:
Build production multi-agent systems using Bedrock Agents — defining supervisor/sub-agent hierarchies, action groups, session memory, and inline agents for complex enterprise workflows. RAG & Knowledge Layer:
Design Knowledge Base pipelines with vector stores (OpenSearch Serverless, Aurora pgvector, Pinecone) — covering chunking strategies, embedding models, hybrid search, and retrieval optimization. Tool & API Integration:
Engineer Lambda-backed action groups (Python/Node.js), API Gateway, and Step Functions to enable agents to invoke enterprise APIs, databases, and third-party systems. Broad-Spectrum AI/ML:
Architect and oversee the full AI lifecycle — classical ML, NLP pipelines, computer vision, fine-tuning, and custom model training on SageMaker. Model & FM Strategy:
Drive selection across Anthropic Claude, Amazon Titan, Meta Llama, Mistral, and open-source LLMs — evaluating latency, cost, context fit, and reasoning fidelity. Enforce Bedrock Guardrails and responsible AI controls at scale. MLOps & Observability:
Define CI/CD pipelines (CDK/CloudFormation) for agent and model deployment; monitor via CloudWatch, Bedrock invocation logs, SageMaker Model Monitor, and X-Ray for cost, drift, and hallucination tracking. Hybrid & Multi-Platform AI:
Integrate AWS-native AI with Azure OpenAI, Vertex AI, or Hugging Face; bridge Bedrock agents with on-premise RPA platforms (UiPath, Automation Anywhere) via event-driven patterns. Executive Advisory:
Lead C-suite workshops to define AI strategy, shape build-vs-buy decisions, and quantify ROI across the AI portfolio Technical Requirements Agent Core & LLM Patterns:
Deep hands-on expertise in agentic reasoning loops (ReAct, Reflexion, Plan-and-Execute), multi-agent orchestration frameworks (LangGraph, AutoGen, CrewAI), prompt engineering, tool-use design, and context window management for long-running autonomous workflows. AWS Bedrock & Agentic Stack:
Proficient with Bedrock Agents, Knowledge Bases, Guardrails, Model Evaluation, and inline agent patterns; experienced in configuring supervisor/sub-agent architectures at enterprise scale. Broad AI/ML Stack:
Hands-on with SageMaker (training, inference, pipelines) and core ML/NLP/CV
frameworks — scikit-learn, HuggingFace Transformers, PyTorch/TensorFlow. Data & Integration Layer:
Strong command of vector DBs (OpenSearch Serverless, Aurora pgvector, Pinecone), RAG pipeline design, AWS Lambda/API Gateway/Step Functions, and event-driven architectures. MLOps & IaC:
AWS CDK/CloudFormation for repeatable deployments; MLflow or SageMaker Experiments for model tracking; CI/CD tooling for agent versioning and rollback. Preferred:
Multi-cloud AI exposure (Azure OpenAI, Vertex AI) • RPA platforms (UiPath, Automation Anywhere) • AWS Certified Solutions Architect - Professional or ML Specialty. Cognizant will only consider applicants for this position who are legally authorized to work in the United States without requiring company sponsorship now or at any time in the future.
