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About This Role
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 a Partner Consulting, Enterprise AI and Manufacturing Technology Strategy , you will make an impact by leading complex enterprise AI, Generative AI, and technology transformation programs for manufacturing and automotive clients. You will be a valued member of the Enterprise AI Consulting Practice and work collaboratively with client executives, enterprise architects, engineering teams, security and data leaders, and cross\-functional delivery teams.
This role combines strategic technology advisory, solution and integration architecture, and hands\-on delivery leadership, with a focus on helping clients modernize enterprise platforms, integrate AI into business and technology workflows, and deliver production\-grade solutions that create measurable business value.
In this role, you will:
Lead multi\-workstream enterprise AI, Generative AI, and technology transformation programs, owning scope, planning, staffing, dependencies, risks, delivery quality, and client communications.
Shape IT strategy, solution architecture, and integration architecture for manufacturing and automotive clients across R\&D, sales and marketing, procurement, supply chain, transportation, and finance.
Architect and guide technical decisions across cloud and data architecture, application modernization, application portfolio assessment, AI in the software development lifecycle, model selection, RAG design, orchestration patterns, and evaluation frameworks.
Partner with enterprise architects and client technology leaders to integrate AI capabilities across ERP, manufacturing SaaS solutions, SCM, CRM, data platforms, identity, middleware, and digital platforms.
Establish engineering and delivery standards, including delivery lifecycle phases, quality gates, testing, observability, release readiness, and reusable delivery assets such as reference architectures, accelerators, evaluation harnesses, and delivery playbooks.
Consistently demonstrate the Cognizant Way to Lead, which means operating with Personal Leadership by building trust, collaboration, and inclusion, Organizational Leadership by driving vision, purpose, and strategic direction, and Business Leadership by exemplifying client focus, accountability, results, and financial acumen.
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 to 20 years of experience in software engineering, solution architecture, enterprise technology delivery, IT strategy, or consulting\-led transformation programs.
Deep understanding of manufacturing and automotive technology ecosystems, including R\&D, sales and marketing, procurement, supply chain, transportation, finance, ERP, and manufacturing SaaS solutions.
Proven experience designing and delivering production enterprise systems, AI\-enabled solutions, or LLM\-powered applications in complex, regulated, or large\-scale enterprise environments.
Strong command of cloud and data architecture, application modernization, application portfolio assessment, enterprise integration, APIs, microservices, event\-driven architecture, security, and observability.
Demonstrated delivery leadership across large transformation programs, including AI, GenAI, ERP modernization, cloud migration, data transformation, or enterprise platform modernization.
Embodiment of the Cognizant Way to Lead: Leading Self, Leading Others, and Leading the Business.
Embodiment of Cognizant’s Values: Work as One, Dare to Innovate, Raise the Bar, Do the Right Thing, and Own It.
These will help you succeed
Experience with RAG pipelines, semantic search, embeddings, vector databases, re\-rankers, context schemas, evaluation frameworks, tool\-use agents, and multi\-step AI workflows.
Deep understanding of enterprise data architecture, governance, lineage, data residency, and operationalization for AI context, retrieval, and decision support.
Experience with prompt engineering, context engineering, structured outputs, agentic systems, orchestration frameworks, DevSecOps, MLOps, or LLMOps practices.
Prior client\-facing consulting, advisory, or embedded engineering experience in a professional services environment.
Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, Business, or a related field. Cloud certifications across AWS, Azure, or GCP are preferred.
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. Compensation
The annual base salary for this position is $162,000 to $295,000 , 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 a competitive benefits package, which may include:
Medical, dental, vision, and life insurance
401(k) plan and contributions
Employee stock purchase plan
Employee assistance program
10 paid holidays plus PTO
Paid parental leave and fertility assistance
Learning and development certifications and programs
Salary Context
This $162K-$295K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($228K) sits 6% above the category median. Disclosed range: $162K to $295K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Cognizant AI Hiring
Cognizant has 24 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, AI Architect, AI Agent Developer. Positions span Juno Beach, FL, US, Pleasanton, CA, US, Rockville, MD, US. Compensation range: $99K - $405K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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
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