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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 6,000\+ 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 an Associate Partner Consulting, Enterprise AI you will make an impact by leading the delivery of complex GenAI programs end\-to\-end, staying close to engineering, and translating business goals into architecture and execution plans. You will be a valued member of the GenAI Consulting team and work collaboratively with managers, primary teams, and client stakeholders.
In this role, you will:
Lead multi\-workstream GenAI delivery programs, including scope, planning, staffing, risk management, dependencies, and stakeholder communications.
Architect and review key technical decisions such as model selection, RAG design, context schemas, orchestration, tool\-use, memory patterns, and evaluation strategy.
Establish engineering standards (ADLC\-style phases, quality gates, testing/evaluations, observability) and ensure teams ship production\-grade solutions.
Partner with enterprise architects to integrate AI capabilities with ERP/SCM/CRM, data platforms, identity, and integration layers.
Convert ambiguous requirements into clear backlogs, technical specifications, and acceptance criteria; drive rapid iteration while protecting security and compliance.
Build reusable assets: reference architectures, accelerators, prompt/context libraries, evaluation harnesses, and delivery playbooks.
Coach and mentor senior engineers and architects; provide technical leadership, code/design reviews, and pragmatic problem solving.
Consistently demonstrate the Cognizant Way to Lead, which means operating with Personal Leadership (building trust, collaboration, and inclusion), Organizational Leadership (driving vision and purpose, demonstrating a strategic and enterprise mindset, and creating and communicating a bold direction that inspires purpose), and Business Leadership (exemplifying client focus, managing ambiguity with accountability and results, and operating with 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 as needed. 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
10–18 years in software engineering/architecture with demonstrable delivery of production systems in enterprise environments.
Hands\-on experience building LLM\-powered applications: RAG pipelines, tool\-use agents, multi\-step workflows; comfortable guiding implementation details.
Strong grasp of cloud\-native architecture (APIs, microservices, event\-driven patterns), plus security and observability best practices.
Practical experience with embeddings, semantic search, vector databases, re\-rankers, and evaluation frameworks for RAG quality.
Experience integrating with enterprise systems (ERP/SCM/CRM), middleware, and data platforms; strong understanding of system\-of\-record semantics.
These will help you succeed
Demonstrated delivery leadership in transformations (ERP programs, modernization, cloud migrations) with real constraints: compliance, data quality, and integration complexity.
Strong understanding of enterprise data architecture (lakes/warehouses, governance, lineage) and how to operationalize it for AI context and retrieval.
Confidence operating in regulated environments and driving controls for privacy, security, auditability, and data residency.
Strong prompt and context engineering: system prompts, structured outputs, tool\-use prompting, and context assembly patterns.
Experience building agentic systems with orchestration frameworks (or custom implementations) and designing safe tool integrations.
Strong communication and executive\-ready storytelling: explain architecture, trade\-offs, and risks to varied audiences.
Delivery mindset: thrives in ambiguity, drives decisions, removes blockers, and raises the bar on engineering quality.
Collaborative leadership: partners effectively with product, security, data, and client stakeholders to move work forward.
Embodiment of the Cognizant Way to Lead: Leading Self, Leading Others, \& Leading the Business.
The embodiment of Cognizant’s Values of: Work as One, Dare to Innovate, Raise the Bar, Do The Right Thing, \& Own It.
Preferred Qualifications
B.Tech / M.Tech in Computer Science, Engineering, or equivalent; cloud certifications (AWS, Azure, GCP).
Experience with DevSecOps, MLOps, or LLMOps practices in enterprise delivery.
Prior client\-facing consulting or embedded engineering roles in a professional services firm.
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
$185,000 \- $218,000
This position is eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans
Benefits
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
Post closing date
Applications will be accepted until 5/31/2026
Salary Context
This $185K-$218K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 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 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 $181,170 based on 12,692 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,880. This role's midpoint ($201K) sits 11% above the category median. Disclosed range: $185K to $218K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Cognizant AI Hiring
Cognizant has 18 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer, AI Architect, Research Scientist. Positions span Santa Clara, CA, US, Warren, MI, US, Atlanta, GA, US. Compensation range: $84K - $280K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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