Job description

Job description: Experienced AWS Agentic AI Engineer to design, build, and deploy autonomous and semi‑autonomous AI agents on AWS. This role focuses on developing goal‑driven, tool‑using, multi‑step AI systems capable of reasoning, planning, executing actions, and collaborating with humans and enterprise systems to solve real‑world business problems. The position sits at the intersection of Large Language Models (LLMs), cloud‑native architecture, APIs, and automation, delivering production‑grade agentic AI solutions using AWS AI/ML and cloud services. Experience & Qualifications • Bachelor’s degree in computer science, Engineering, Technology, or a related field, with 8–12 years of overall IT experience and at least 3+ years of hands‑on experience building agentic AI solutions using AWS cloud‑native services. • Strong expertise in core AWS services including AWS Lambda, Step Functions, EventBridge, S3, DynamoDB or Aurora, OpenSearch, Amazon Bedrock, and Amazon SageMaker. • Advanced proficiency in Python for building scalable AI systems and automation workflows. • Proven experience leading the technical design and implementation of complex AI agent architectures and components. • Hands‑on experience designing Retrieval Augmented Generation (RAG) solutions and applying model fine‑tuning techniques. • Demonstrated ability to manage performance, scalability, reliability, and cost optimization of AI workloads in production environments. • Proven experience conducting code reviews, leading knowledge‑sharing sessions, and making critical decisions on technologies, architectures, and frameworks. • Healthcare domain experience is desirable, along with knowledge of AI safety, governance, compliance, and responsible AI practices. • AWS certifications such as Solutions Architect or Machine Learning Specialty are preferred, and experience with LangChain and LangGraph is a plus. • Excellent communication skills with a strong ability to collaborate effectively with both technical and non‑technical stakeholders. Roles & Responsibilities • Design and develop agentic AI systems using Amazon Bedrock and other foundation models, including Claude and Titan. • Build autonomous and semi‑autonomous AI agents capable of multi‑step reasoning, planning, tool usage, action execution, and human‑in‑the‑loop collaboration. • Integrate with Amazon SageMaker for custom model training, fine‑tuning, evaluation, and experimentation. • Implement Retrieval Augmented Generation (RAG) solutions using Amazon OpenSearch, Amazon Aurora, DynamoDB, and vector databases such as FAISS or Pinecone. • Optimize inference cost, latency, scalability, and reliability across AI workloads. • Implement guardrails, validation layers, and human‑in‑the‑loop controls to ensure safe, reliable, and predictable AI behavior. • Address hallucination mitigation, prompt injection risks, bias concerns, and model misuse scenarios. • Ensure compliance with enterprise AI governance, security, and regulatory standards. • Implement comprehensive logging, monitoring, observability, and audit trails for AI systems. • Support production deployments, incident resolution, and root cause analysis for AI services. • Continuously improve agent performance, reliability, robustness, and usability through iteration, monitoring, and feedback. • Collaborate closely with architecture, DevOps, data engineering, security, and business teams to deliver end‑to‑end AI solutions. Additional employment information Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law. Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview. Cognizant is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, state or local laws. Applicants for US based positions must be currently authorized to work for any employer in the United States. The company is unable to provide employment-based immigration sponsorship for US based positions. If you have a disability that requires reasonable accommodation to search for a job opening or submit an application, please email CareersNA2@cognizant.com for roles based in the Americas or CareersIndia2@cognizant.com for roles based in India. About Cognizant: Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization’s unique context into technology systems that amplify human potential, drive tangible outcomes and keep global enterprises ahead in a fast-changing world. See how at cognizant.ai or @cognizant.

Requirements

Must-haves

The position sits at the intersection of Large Language Models (LLMs), cloud‑native architecture, APIs, and automation, delivering production‑grade agentic AI solutions using AWS AI/ML and cloud services.
Experience & Qualifications
Strong expertise in core AWS services including AWS Lambda, Step Functions, EventBridge, S3, DynamoDB or Aurora, OpenSearch, Amazon Bedrock, and Amazon SageMaker.
Advanced proficiency in Python for building scalable AI systems and automation workflows.
Proven experience leading the technical design and implementation of complex AI agent architectures and components.
Hands‑on experience designing Retrieval Augmented Generation (RAG) solutions and applying model fine‑tuning techniques.
Demonstrated ability to manage performance, scalability, reliability, and cost optimization of AI workloads in production environments.
Proven experience conducting code reviews, leading knowledge‑sharing sessions, and making critical decisions on technologies, architectures, and frameworks.
Healthcare domain experience is desirable, along with knowledge of AI safety, governance, compliance, and responsible AI practices.
Excellent communication skills with a strong ability to collaborate effectively with both technical and non‑technical stakeholders.
Roles & Responsibilities
Design and develop agentic AI systems using Amazon Bedrock and other foundation models, including Claude and Titan.
Build autonomous and semi‑autonomous AI agents capable of multi‑step reasoning, planning, tool usage, action execution, and human‑in‑the‑loop collaboration.
Integrate with Amazon SageMaker for custom model training, fine‑tuning, evaluation, and experimentation.
Implement Retrieval Augmented Generation (RAG) solutions using Amazon OpenSearch, Amazon Aurora, DynamoDB, and vector databases such as FAISS or Pinecone.
Optimize inference cost, latency, scalability, and reliability across AI workloads.
Implement guardrails, validation layers, and human‑in‑the‑loop controls to ensure safe, reliable, and predictable AI behavior.
Address hallucination mitigation, prompt injection risks, bias concerns, and model misuse scenarios.
Ensure compliance with enterprise AI governance, security, and regulatory standards.
Implement comprehensive logging, monitoring, observability, and audit trails for AI systems.
Support production deployments, incident resolution, and root cause analysis for AI services.
Continuously improve agent performance, reliability, robustness, and usability through iteration, monitoring, and feedback.
Collaborate closely with architecture, DevOps, data engineering, security, and business teams to deliver end‑to‑end AI solutions.

Nice-to-haves

AWS certifications such as Solutions Architect or Machine Learning Specialty are preferred, and experience with LangChain and LangGraph is a plus.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

awsaimlpythonmachine learning

Platform: cognizant
Effort: medium