Forward Deployed AI Engineer – Enterprise AI Architecture & Responsible AI

Practice - AIA - Artificial Intelligence and Analytics

About AI & Analytics: Artificial intelligence (AI) and the data it collects and analyzes will soon sit at the core of all intelligent, human-centric businesses. By decoding customer needs, preferences, and behaviors, our clients can understand exactly what services, products, and experiences their consumers need. Within AI & Analytics, we work to design the future—a future in which trial-and-error business decisions have been replaced by informed choices and data-supported strategies.

By applying AI and data science, we help leading companies to prototype, refine, validate, and scale their AI and analytics products and delivery models. Cognizant’s AIA practice takes insights that are buried in data and provides businesses a clear way to transform how they source, interpret and consume their information. Our clients need flexible data structures and a streamlined data architecture that quickly turns data resources into informative, meaningful intelligence.

Job Summary

We are seeking a senior Forward Deployed AI Engineer to architect, build, and operationalize enterprise-scale AI systems for complex client transformation programs. This role combines hands-on engineering with enterprise AI architecture, responsible AI governance, and executive-level technical advisory. The engineer will work alongside client and delivery teams to design human-agent operating models, agentic workflows, context pipelines, and regulated AI decisioning solutions. Success will be measured by sustainable customer outcomes, production adoption, and the capability transferred to client teams.

In this role, you will:

• Design and deliver AI-native enterprise architectures spanning applications, data platforms, business processes, and cloud environments.
• Build production-grade Generative AI, RAG, and agentic AI solutions, taking ownership from architecture and prototyping through deployment and operation.
• Define human-agent capability matrices, operating models, escalation paths, and human-in-the-loop controls for enterprise workflows.
• Architect context pipelines, knowledge orchestration frameworks, multi-agent systems, and cross-platform AI integrations.
• Develop guardrail specifications covering agent boundaries, tool access, data handling, autonomy, safety, and exception management.
• Establish responsible AI controls for bias detection, explainability, transparency, auditability, model risk, and regulatory compliance.
• Lead client architecture workshops, technical discovery sessions, design reviews, and executive-level solution discussions.
• Define standards for AI-generated code quality, validation, security, testing, observability, and production readiness across the AI application development lifecycle.
• Evaluate frontier models through structured benchmarking, red teaming, safety testing, and business-use-case validation.
• Mentor AI engineers and architects while developing reusable reference architectures, accelerators, playbooks, and delivery standards.

What you need to have to be considered

• 12–20 years of experience in enterprise technology, including significant leadership in AI architecture, solution architecture, or digital transformation.
• Proven experience designing and delivering enterprise-scale Generative AI, RAG, LLM, and agentic AI solutions in production environments.
• Strong hands-on software engineering experience with Java, .NET, Python, or comparable enterprise application technologies.
• Deep understanding of multi-agent architectures, context engineering, knowledge orchestration, tool use, memory, planning, and human-agent collaboration patterns.
• Experience defining responsible AI frameworks, agent guardrails, evaluation standards, audit controls, and regulated AI decisioning models.
• Strong knowledge of cloud-native AI architecture, APIs, microservices, data platforms, DevSecOps, MLOps, AIOps, and enterprise integration patterns.
• Experience evaluating frontier models, conducting AI red-team exercises, and defining AI quality, security, performance, and safety criteria.
• Demonstrated ability to lead client architecture forums and advise executive, business, risk, and technology stakeholders.
• Experience delivering AI solutions in regulated industries such as financial services, healthcare, life sciences, insurance, or government is preferred.
• Strong technical leadership, consulting, communication, stakeholder m…