Lead Full stack AI Engineer

Lead Full Stack AI Engineer Cognizant is seeking an accomplished Lead Full Stack AI Engineer to provide hands-on technical leadership in designing, developing, and deploying enterprise-scale AI solutions powered by AWS cloud technologies, Generative AI, and Agentic AI frameworks. This role combines deep software engineering expertise with advanced AI solution architecture and cloud-native development. The successful candidate will lead the delivery of scalable, secure, reliable, and cost-optimized AI applications while driving engineering best practices and mentoring development teams. The ideal candidate will possess strong experience building LLM-powered applications, Retrieval-Augmented Generation (RAG) solutions, agentic workflows, cloud-native architectures, and enterprise-scale AI platforms. This position follows a hybrid work model based in New Jersey. Candidates local to New Jersey are strongly preferred and should be available to work onsite as required by client and business needs. Key ResponsibilitiesAI Solution Architecture & Engineering Lead the architecture, design, development, and production deployment of enterprise AI solutions spanning: Traditional Machine Learning, Generative AI, Agentic AI.Design scalable, secure, and production-ready AI applications that align with enterprise architecture standards. Translate business requirements into technical architectures and implementation plans. Drive solution design decisions balancing scalability, maintainability, performance, security, and cost optimization. Full Stack Application Development Provide hands-on engineering leadership across Front-End Applications, Backend Services, APIs, Microservices, AI Orchestration Layers, Enterprise Integrations. Develop modern user experiences using React or comparable front-end frameworks. Design robust backend services using Python, FastAPI, and cloud-native development practices. Build enterprise-grade distributed systems and event-driven architectures. Generative AI & Agentic AI Solutions Design and implement advanced AI solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Knowledge Management Platforms, Enterprise Search Solutions. Develop agentic workflows using AWS Bedrock AgentCore, LangGraph, Strands Agents, Modern AI Orchestration Frameworks. Implement multi-agent systems supporting Tool Calling, MCP Integration, Agent Memory, State Management, Workflow Orchestration. Optimize AI solution performance, accuracy, reliability, and explainability. Enterprise AI Platform Integration Architect integrations with enterprise AI platform capabilities including AI Gateway, MCP Gateway, Agent & Tool Registry, Knowledge Management Platforms, RAG Services, Enterprise Search Services, Agent Memory Frameworks, Observability Platforms. Ensure seamless integration across enterprise applications and AI ecosystems. Design reusable AI services and platform accelerators. AWS Cloud Engineering Leverage AWS services to build secure, highly available, and cost-efficient AI platforms. Lead implementations utilizing AWS Bedrock, Amazon SageMaker, AWS Lambda, ECS/EKS, Amazon S3, API Gateway, Step Functions, CloudWatch, OpenSearch. Architect cloud-native applications following AWS best practices. Optimize cloud infrastructure performance, scalability, and cost management. DevOps, MLOps & Production Operations Establish engineering best practices across CI/CD Pipelines, Infrastructure as Code, Automated Testing, Release Management, Production Readiness. Utilize Docker, Kubernetes, Terraform, GitHub Actions. Drive application monitoring, troubleshooting, observability, and production support. Implement automated deployment and operational excellence frameworks. Security, Governance & Responsible AIImplement enterprise security controls including Authentication, Authorization, Identity Management, Data Privacy, Governance Controls. Support Responsible AI initiatives through monitoring, validation, and governance frameworks. Ensure compliance with industry regulations and enterprise security requirements. Implement guardrails, auditability, and observability controls for AI applications. Technical Leadership & Mentorship Conduct architecture reviews, design reviews, and code reviews. Mentor engineers and provide technical leadership across development teams. Establish software engineering standards, reusable frameworks, and development best practices. Collaborate with architects, engineers, product owners, data scientists, and cloud teams to deliver successful solutions. Drive innovation and continuous improvement initiatives across AI and software engineering programs.