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About the Role
The AI Strategist plays a pivotal role in shaping Cognizant’s AI portfolio by designing high impact AI use cases, building compelling value propositions, and enabling early\-stage client engagement. Sitting at the intersection of business design, AI capability, and industry needs, this role transforms opportunity spaces into structured, scalable, commercially strong AI offerings that drive growth for the AI Market Unit. Impact AI use cases, building compelling value propositions, and enabling early‑stage client engagement. Sitting at the intersection of business design, AI capability, and industry needs, this role transforms opportunity spaces into structured, scalable, commercially strong AI offerings that drive growth for the AI Market Unit.
This role is ideal for a product\-minded consultant or solution designer who can blend conceptual thinking, business acumen, and AI literacy to build differentiated offerings that win in the market. Minded consultant or solution designer who can blend conceptual thinking, business acumen, and AI literacy to build differentiated offerings that win in the market.
Key Responsibilities
1\. AI Offering \& Use Case Design
Own conceptual and architectural design for prioritized AI use cases and offerings.
Translate business problems, industry signals, and AI capabilities into structured solution blueprints.
Develop narratives, workflows, and design artifacts that illustrate the “art of the possible” and competitive differentiation.
2\. Value Proposition Development \& Collateral Creation
Build business value frameworks, impact models, and client\-ready point of view documents of‑view documents.
Create reusable offering assets including pitch decks, one\-pagers, POVs, demo scripts, and solution differentiators.
Align offerings with market demand, competitive insights, and Cognizant’s internal capability landscape.
3\. Client \& Internal Engagement
Partner with clients, sales teams, ISLs, consulting, and pre‑sales in early discovery phases.
Lead ideation sessions and offer walkthroughs to surface use cases and refine solution approaches.
Support pursuit teams with value narratives, offering insights, and strategic justification for AI\-led transformation programs.
Decision Rights
Owns
Contribution to the AI offering portfolio strategy—prioritization, roadmap, and investment inputs.
Offering qualification and readiness criteria for GTM launch.
Approval of pricing models, ROI frameworks, and sales kits used across pursuits.
Accountability for offering lifecycle performance (revenue, adoption, renewals).
Influences
GTM and market positioning for AI offerings.
Field enablement through playbooks, kits, and value selling tools.
Engineering decisions to ensure offerings are scalable and deployment ready.
Key Performance Indicators
Number of new AI offerings developed.
Offering attach rate across accounts.
Total contract value (TCV) driven by AI offerings.
Stakeholder Interactions
Internal
AI Business \& Design Leadership
Pre‑Sales Specialists
Ecosystem Leaders
AI Specialist Sales
Service Line \& Business Unit Heads
External
Business Owners / Functional Leaders (Supply Chain, Finance, CX/CRM, Healthcare Ops)
Product Managers
Data Science \& Analytics Teams
IT \& Enterprise Architecture Teams
Cloud \& Platform Engineering Teams
Operations \& Transformation Leaders
Security \& Compliance Teams
Experience Required
Min 15 years of consulting, solution design, digital/AI strategy, or product/experience design.
Proven experience framing business problems and structuring AI or digital solutions.
Exposure to AI platforms, GenAI toolchains, and industry specific AI use specific cases.
Strong commercial acumen and ability to tie technology to business value.
Demonstrated stakeholder management and cross functional collaboration.
Key Competencies
Strong conceptual and structured problem\-solving skills.
Ability to blueprint solutions and articulate complex ideas simply.
Business storytelling and narrative building for executive audiences.
Ability to work seamlessly with sales, engineering, consulting, and product teams.
Understanding of AI/ML concepts at a business technical level (not necessarily hands on technical).
Salary and Other Compensation :
Applications will be accepted until Aug 30, 2026\.
The annual base salary for this position is between $200,000 \- $275,000 depending on the experience and other qualifications of the successful candidate.
This position is also eligible for Cognizant’s discretionary annual incentive program and stock awards, based on performance and subject to the terms of Cognizant’s applicable plans.
Benefits : Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:
Medical/Dental/Vision/Life Insurance
Paid holidays plus Paid Time Off
401(k) plan and contributions
Long\-term/Short\-term Disability
Paid Parental Leave
Employee Stock Purchase Plan
Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Salary Context
This $200K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($237K) sits 9% above the category median. Disclosed range: $200K to $275K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Cognizant AI Hiring
Cognizant has 22 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Architect. Positions span Irving, TX, US, Louisville, KY, US, New York, NY, US. Compensation range: $85K - $435K.
Location Context
AI roles in Seattle pay a median of $236,900 across 267 tracked positions. That's 9% above the national 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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