Interested in this AI/ML Engineer role at Cognizant?
Apply Now →Skills & Technologies
About This Role
Position Summary
The AI Platform Strategist is responsible for defining and driving the enterprise AI platform vision, strategy, and roadmap on AWS. This role serves as the bridge between business leadership, data teams, cloud engineering, architecture, and AI practitioners to design a scalable, secure, and governed AI ecosystem that accelerates innovation and business value.
The strategist leads platform planning activities, assesses current-state AI and data capabilities, defines future-state architectures, establishes governance frameworks, and develops implementation roadmaps for AI, Generative AI, Agentic AI, and advanced analytics solutions built on AWS.
Key Responsibilities
AI Platform Strategy & Roadmap
- Define the enterprise AI platform vision and target operating model.
- Develop multi-year AI platform roadmaps aligned to business and technology objectives.
- Assess current AI, data, analytics, and cloud capabilities.
- Identify platform gaps, dependencies, and modernization priorities.
- Establish phased implementation plans and investment recommendations.
AWS AI & Data Architecture Strategy
- Define target-state AI platform architecture leveraging AWS services.
- Develop architecture patterns for: + Generative AI + Agentic AI + Machine Learning + Data Science Workbenches + Model Lifecycle Management + AI Application Development
- Establish design principles for scalability, interoperability, security, and reusability.
- Guide integration of AI capabilities with enterprise applications and business workflows.
AI Services & Platform Capability Development
Lead strategy for platform capabilities including:
- Amazon Bedrock
- Amazon SageMaker
- AWS Glue
- Amazon EMR
- Amazon Redshift
- Amazon OpenSearch
- Amazon Kendra
- Amazon EKS
- AWS Lambda
- AWS Step Functions
Define platform services such as:
- Prompt Management
- Model Catalogs
- Feature Stores
- Vector Databases
- RAG Frameworks
- Agent Orchestration
- MLOps
- LLMOps
- API Management
- AI Monitoring and Observability
AI Governance & Responsible AI
- Define AI governance frameworks and operational controls.
- Establish model governance and lifecycle management processes.
- Create policies for: + Responsible AI + Data Privacy + Security + Compliance + Risk Management
- Define AI guardrails and human-in-the-loop review processes.
- Align platform capabilities with industry and regulatory requirements.
Demand Management & Use Case Prioritization
- Design frameworks for intake, assessment, and prioritization of AI opportunities.
- Create value-based scoring methodologies.
- Align business demand with platform capabilities and implementation capacity.
- Develop repeatable processes for onboarding new AI use cases.
- Maintain enterprise AI opportunity portfolios and roadmaps.
Operating Model & Organizational Readiness
- Define AI platform ownership and organizational structures.
- Establish AI Centers of Excellence (CoE).
- Develop platform support and service models.
- Recommend talent, skills, and capability-building strategies.
- Drive adoption and change management initiatives.
Executive Advisory
- Advise executive stakeholders on AI platform investments and priorities.
- Present AI strategy, architecture, and roadmap recommendations.
- Develop board-level and executive business cases.
- Align platform initiatives to measurable business outcomes and ROI.
Required Qualifications
- 10+ years of experience in enterprise architecture, cloud strategy, data strategy, AI strategy, or digital transformation.
- 5+ years working with AWS cloud technologies.
- Experience designing enterprise AI or data platforms.
- Strong understanding of: + Generative AI + Agentic AI + Machine Learning + Data Platforms + MLOps and LLMOps + Cloud-Native Architectures
- Experience developing executive-level roadmaps and business cases.
- Strong communication and stakeholder management skills.
Preferred Qualifications
AWS Certifications
- AWS Certified Solutions Architect Professional
- AWS Certified Machine Learning Specialty
- AWS Certified Data Engineer
- AWS Certified AI Practitioner
Industry Experience
- Healthcare
- Payer
- Provider
- Life Sciences
- Highly regulated industries
Additional Experience
- Databricks
- Snowflake
- Collibra
- Informatica
- Data Governance Platforms
- AI Governance Platforms
Key Deliverables
- Enterprise AI Platform Strategy
- AWS AI Reference Architecture
- AI Capability Assessment
- AI Maturity Assessment
- Platform Roadmap (12-36 Months)
- AI Governance Framework
- Responsible AI Framework
- AI Operating Model
- Use Case Prioritization Framework
- AI Service Catalog
- MLOps/LLMOps Strategy
Business Value Realization Framework
We're excited to meet people who share our mission and can make an impact in a variety of ways. Don't hesitate to apply, even if you only meet the minimum requirements listed. Think about your transferable experiences and unique skills that make you stand out as someone who can bring new and exciting things to this role.
Salary and Other Compensation:
Applications will be accepted until Nov 15, 2026.
The annual salary for this position is between $80,000 - $180,000 depending on experience and other qualifications of the successful candidate.
This position is also eligible for Cognizant’s discretionary annual incentive program, 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 $80K-$180K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($130K) sits 39% below the category median. Disclosed range: $80K to $180K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Cognizant AI Hiring
Cognizant has 49 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer, Data Scientist, AI Architect. Positions span Bentonville, AR, US, Washington, DC, London Area, United Kingdom. Compensation range: $100K - $204K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% of all AI roles offer remote work.
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.