Partner Consulting, AI

About Cognizant Consulting Cognizant Consulting is more than Cognizant’s consulting practice—we’re a global community of 5,000+ experts dedicated to helping clients reimagine their business. By combining deep industry knowledge with advanced technology advisory capabilities, we deliver innovative, high‑impact solutions to Fortune 500 and Global 2000 clients.

About The Role As a Partner Consulting – AI , you will make an impact by leading end‑to‑end delivery of complex Generative AI and enterprise AI transformation programs , while staying closely engaged in architecture and engineering decisions.

You will be a valued member of the Enterprise AI Consulting Practice and work collaboratively with client executives, enterprise architects, engineering teams, and security/data leaders to design, build, and deploy production‑grade AI solutions that deliver measurable business value.

This role emphasizes delivery leadership and hands‑on technical depth , with less focus on executive sponsorship and more accountability for solution quality, execution rigor, and team outcomes.

In This Role, You Will

• Lead multi‑workstream enterprise AI and GenAI delivery programs, owning scope, planning, staffing models, dependencies, risk management, and client communications
• Architect and oversee key technical decisions, including model selection, RAG design, context schemas, orchestration patterns, tool‑use strategies, memory design, and evaluation frameworks
• Establish and enforce engineering and delivery standards (ADLC‑style phases, quality gates, testing/evaluations, observability, and release readiness) to ensure production‑grade outcomes
• Partner with enterprise architects to integrate AI capabilities across ERP, SCM, CRM, data platforms, identity, and middleware layers
• Translate ambiguous business requirements into clear backlogs, technical specifications, and acceptance criteria, enabling rapid iteration while protecting security and compliance
• Build reusable AI delivery assets, including reference architectures, accelerators, prompt and context libraries, evaluation harnesses, and delivery playbooks
• Coach and mentor senior engineers and architects through design reviews, code reviews, and hands‑on problem solving, raising the bar on engineering quality across teams
• Consistently demonstrate the Cognizant Way to Lead, balancing client focus, delivery accountability, and financial discipline

Work Model We believe hybrid work is the way forward as we strive to provide flexibility wherever possible.

Based on this role’s business requirements, this is a remote position ; however, the role requires a hybrid and travel‑based delivery model , including travel to client sites and Cognizant offices.

The working arrangements for this role are accurate as of the date of posting and may change based on project or client needs. We will always be clear about role expectations.

What You Must Have To Be Considered

• 15–20 years of experience in software engineering, solution architecture, or enterprise technology delivery
• Proven experience delivering production enterprise systems in complex, regulated environments
• Hands‑on experience designing and guiding delivery of LLM‑powered applications, including RAG pipelines, tool‑use agents, and multi‑step workflows
• Strong command of cloud‑native architectures (APIs, microservices, event‑driven systems), plus security and observability best practices
• Practical experience with embeddings, semantic search, vector databases, re‑rankers, and RAG evaluation frameworks
• Experience integrating AI solutions with enterprise platforms, data systems, and systems‑of‑record
• Demonstrated delivery leadership across large transformation programs (ERP modernization, cloud migration, data transformation)
• Comfort operating in regulated environments, with strong attention to privacy, security, auditability, and data residency

Embodiment of the Cognizant Way to Lead: Leading Self, Leading Others, and Leading the Business.

These Will Help You Succeed

• Deep understanding of enterprise data architecture, governance, lineage, and operationalization for AI context and retrieval
• Strong experience with prompt and context engineering, including structured outputs and tool‑use prompting
• Experience designing agentic systems using orchestration frameworks or custom implementations
• Exposure to…