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L\&A and AI Growth and Competency Leader
Leading at Cognizant
This is a Leadership (D\+) role at Cognizant. We believe how you lead is as important as what you deliver. Cognizant leaders at every level: Drive our business strategy and inspire teams around our future. Live the leadership behaviors , leading themselves, others and the business. Uphold our Values , role modeling them in every action and decision. Nurture our people and culture , creating a workplace where all can thrive.
At Cognizant, leadership transcends titles and is embodied in actions and behaviors. We empower our leaders at every level to drive business strategy, inspire teams, uphold our values, and foster an inclusive culture.
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. Blending our deep industry and technology advisory capability, we create innovative business solutions for Fortune 500 clients. And now, we’re looking for our next colleague who’ll join us in shaping the future of business. Could it be you?
About the role
As the L\&A and AI Growth and Competency Leader , you will drive the growth, strategy, and execution of AI\-led consulting capabilities across Insurance Consulting. You will lead revenue generation, develop market\-leading offerings, and build high\-performing teams while working closely with global stakeholders, clients, and service lines.
In this role, you will:
Own and grow a consulting portfolio with direct revenue responsibility and pipeline expansion across AI\-led services
Define and execute portfolio strategy, including AI\-driven offerings, capability development, and go\-to\-market planning
Drive business development through deal origination, pipeline creation, and improved win rates on large consulting engagements
Lead and scale competency development, including hiring strategy, training programs, and capability alignment to market demand
Build and manage senior client relationships while positioning Cognizant as a trusted advisor in AI and transformation
Champion thought leadership, developing market insights and supporting strategic partnerships and alliances
Consistently demonstrate the Cognizant Way to Lead across personal, organizational, and business leadership dimensions
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 hybrid position requiring X days per week in a client or Cognizant office in XX, United States , along with travel as needed. Regardless of your working arrangement, we support a healthy work\-life balance through our wellbeing programs.
The working arrangement is accurate at the time of posting and may evolve based on project and client needs.
What you must have to be considered
15\+ years of experience in consulting, with a strong track record of selling and delivering technology\-enabled business transformation
Proven leadership in AI, analytics, or digital transformation within the Insurance or Financial Services domain
Demonstrated ability to own revenue targets, build pipeline, and close large consulting deals
Experience building and scaling consulting teams, including hiring, capability development, and performance management
Strong executive presence with the ability to engage senior client stakeholders and influence strategic outcomes
Embodiment of the Cognizant Way to Lead: Leading Self, Leading Others, and Leading the Business
These will help you succeed
Deep knowledge of Insurance industry trends, challenges, and AI\-driven transformation opportunities
Experience developing and launching new consulting offerings or practices
Strong collaboration across global teams, service lines, and partner ecosystems
Thought leadership demonstrated through publications, speaking engagements, or client advisory work
Ability to operate in a high\-growth, matrixed environment with competing priorities
Benefits
Medical, dental, vision, and life insurance
401(k) plan with company contributions
Employee stock purchase plan
Employee assistance program
Paid time off and 10 company holidays
Paid parental leave and fertility support
Professional development, certifications, and learning programs
Post closing date
Applications will be accepted until June 17 th , 2026
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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 in Demand for This Role
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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Cognizant AI Hiring
Cognizant has 16 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Research Engineer, Research Scientist. Positions span Seattle, WA, US, Plano, TX, US, Rockville, MD, US. Compensation range: $84K - $218K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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