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About This Role
Role Overview
Cognizant is seeking an experienced Technical Project Manager – AI Document Processing & Authoring to lead the planning, coordination, and delivery of AI-powered document processing and authoring initiatives.
This role requires a blend of project management, business analysis, stakeholder engagement, and technical delivery expertise. The successful candidate will have experience managing AI-focused projects and a strong understanding of the AI-powered document processing and authoring lifecycle, from business requirements gathering through implementation, testing, deployment, and production support.
The ideal candidate will serve as the bridge between business stakeholders, product owners, architects, developers, and platform teams to ensure that business objectives are translated into successful technology solutions.
Work Model
This position is fully remote within the United States, offering flexibility while collaborating with distributed teams across multiple locations and time zones.
Occasional travel may be required for client workshops, planning sessions, or strategic project meetings.
Key Responsibilities
Project Leadership & Delivery Management
- Lead end-to-end project execution for AI-powered document processing and authoring initiatives.
- Develop and manage project plans, timelines, milestones, deliverables, and resource requirements.
- Ensure projects are delivered on schedule, within scope, and aligned with business objectives.
- Monitor project performance and proactively address risks, issues, and dependencies.
- Facilitate project status reviews, steering committee meetings, and stakeholder communications.
Business Analysis & Requirements Management
- Partner with business stakeholders to gather, analyze, and document business requirements.
- Translate business requirements into detailed functional and technical specifications.
- Develop process flows, business workflows, user stories, and acceptance criteria.
- Ensure requirements are clearly documented and understood by technical delivery teams.
- Identify edge cases, business scenarios, and validation criteria to support successful solution delivery.
AI Solution Enablement
- Support AI-powered document processing and authoring initiatives throughout the project lifecycle.
- Collaborate with architects and technical teams to define AI solution requirements and business outcomes.
- Utilize prompt engineering concepts to support AI use-case development and optimization.
- Assist in Codex prompting activities and AI-output validation processes.
- Develop examples, use cases, and testing scenarios to improve AI performance and user adoption.
- Coordinate business reviews of AI-generated outputs and ensure alignment with expected outcomes.
Stakeholder & Cross-Functional Collaboration
- Act as the primary liaison between business users, product owners, developers, architects, QA teams, and platform partners.
- Manage stakeholder expectations and provide regular project updates and communications.
- Facilitate workshops, requirement sessions, user demonstrations, and solution reviews.
- Drive collaboration across cross-functional teams to ensure successful project execution.
Testing, Validation & Quality Assurance
- Coordinate testing activities, including: + Functional Testing + Integration Testing + User Acceptance Testing (UAT) + AI Output Validation
- Ensure business requirements and acceptance criteria are met before release.
- Manage defect triage activities and coordinate issue resolution efforts.
- Support quality assurance processes throughout the solution lifecycle.
Documentation & Process Governance
- Create and maintain project documentation, business requirements, process documentation, project plans, release communications, and operational guides.
- Ensure project artifacts are complete, accurate, and aligned with organizational standards.
- Support knowledge transfer and operational readiness activities.
- Maintain project governance documentation and audit-ready records.
Risk & Dependency Management
- Identify, track, and mitigate project risks and dependencies.
- Escalate critical issues and blockers when appropriate.
- Coordinate project activities across multiple teams and workstreams.
- Ensure alignment between business priorities, technical capabilities, and project delivery objectives.
Required Qualifications
- Bachelor's degree in Business, Information Technology, Computer Science, Engineering, or a related field, or equivalent work experience.
- 10+ years of experience in Technical Project Management, Business Analysis, or Technology Delivery roles.
- Experience managing AI, Digital Transformation, Data, or Enterprise Technology projects.
- Strong experience gathering and analyzing business requirements.
- Proven ability to translate business needs into technical requirements and actionable delivery plans.
- Experience developing: + User Stories + Acceptance Criteria + Business Process Flows + Functional Requirements Documentation
- Knowledge of: + Prompt Engineering + AI-Output Validation + AI Document Processing Workflows + AI Authoring Solutions
- Strong project planning, coordination, and execution skills.
- Experience managing project timelines, risks, dependencies, and stakeholder expectations.
- Understanding of software development lifecycle (SDLC) methodologies.
- Knowledge of enterprise system integrations and API-based architectures.
- Strong written, verbal, presentation, and facilitation skills.
- Excellent organizational, analytical, and problem-solving abilities.
Preferred Qualifications
- Experience supporting Generative AI or Intelligent Automation initiatives.
- Familiarity with AI-powered document processing or content generation platforms.
- Experience working within Agile, Scrum, or hybrid project delivery frameworks.
- Understanding of cloud-based architectures and enterprise platforms.
- Experience with JIRA, Azure DevOps, Confluence, or similar project management tools.
- Project Management Professional (PMP), Certified Scrum Master (CSM), or equivalent certification.
- Experience managing distributed global teams and enterprise-scale programs.
Technical Knowledge Areas
Candidates should demonstrate familiarity with:
- Technical Project Management
- Business Analysis
- Generative AI
- Prompt Engineering
- AI Output Validation
- User Story Development
- Acceptance Criteria Definition
- Software Development Lifecycle (SDLC)
- API Integrations
- Enterprise Applications
- Agile Delivery Methodologies
- Risk Management
- Stakeholder Management
- Process Documentation
- User Acceptance Testing (UAT)
Salary and Other Compensation
The annual salary for this position is anticipated to be between $130,000 and $145,000 USD, depending on experience, qualifications, geographic location, and other factors permitted by law.
In addition to base salary, this position may be eligible for:
- Annual performance-based bonus
- Merit-based compensation increases
- Employee referral bonuses, where applicable
The compensation information provided is accurate as of the date of this posting. Cognizant reserves the right to modify compensation information based on business needs and applicable laws.
Benefits
Cognizant offers a comprehensive benefits package, including:
- Medical, Dental, and Vision Insurance
- Health Savings Account (HSA) and Flexible Spending Accounts (FSA)
- Life Insurance and AD&D Coverage
- Short-Term and Long-Term Disability Insurance
- Paid Time Off (PTO) and Company Holidays
- 401(k) Plan with Company Contributions (subject to eligibility)
- Employee Stock Purchase Plan (where available)
- Employee Assistance Program (EAP)
- Parental Leave and Family Care Benefits
- Tuition Reimbursement and Professional Development Programs
- Access to Learning, Certification, and Career Development Opportunities
Benefits are subject to eligibility requirements and plan terms.
Why Cognizant
- Help drive innovative AI-powered business solutions for leading global organizations.
- Work at the intersection of business strategy, technology delivery, and Generative AI.
- Collaborate with experienced architects, engineers, product teams, and business leaders.
- Gain exposure to emerging AI technologies and enterprise transformation initiatives.
- Access ongoing professional development and career growth opportunities.
- Be part of a global organization committed to innovation, excellence, and client success.
Equal Opportunity Employer
Cognizant is an equal opportunity employer. We are committed to fostering an inclusive workplace where all associates feel valued, respected, and empowered to contribute their unique perspectives. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability, protected veteran status, or any other characteristic protected by applicable law.
Applications will be accepted until the position is filled.
Salary Context
This $130K-$145K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Cognizant, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($137K) sits 35% below the category median. Disclosed range: $130K to $145K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Cognizant AI Hiring
Cognizant has 41 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Data Scientist, AI Safety. Positions span Tampa, FL, US, Washington, DC, Hartford, CT. Compensation range: $100K - $204K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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