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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 experts dedicated to helping clients reimagine their business. Blending deep industry knowledge with advanced technology capabilities, we deliver transformative solutions to Fortune 500 clients. About the Role
As a Senior Manager – AI Engineer , you will play a critical hands-on leadership role in designing, building, and scaling enterprise-grade AI solutions. You will focus on LLM-powered systems, retrieval-augmented generation (RAG), and emerging agentic workflows, translating advanced AI concepts into production-ready applications.
You will be a key member of the AI & Data Consulting team, working closely with architects, clients, and engineering teams to deliver high-quality, scalable AI solutions that drive measurable business outcomes. In this role, you will:
Design, develop, and deploy enterprise-grade AI/LLM solutions, including context handling, memory patterns, and prompt engineering strategies
Build and optimize RAG pipelines , including embeddings, retrieval strategies, re-ranking, and evaluation frameworks for quality and performance
Lead the development of agentic workflows and AI-powered applications, integrating APIs, enterprise tools, and data systems with appropriate guardrails
Collaborate with architects and business stakeholders to integrate AI solutions with enterprise platforms (ERP, CRM, data platforms), ensuring security, scalability, and data integrity
Own end-to-end engineering delivery, including code quality, testing, deployment, and performance optimization
Provide technical leadership by mentoring junior engineers, conducting code reviews, and driving best practices in AI engineering and MLOps
Partner with cross-functional teams to ensure alignment of AI solutions with business needs and production readiness standards Work Model
We strive to provide flexibility wherever possible. Based on this role’s business requirements, this position requires travel as needed and a hybrid work model, with time spent in a client or Cognizant office in the United States. We are committed to supporting a healthy work-life balance through our wellbeing programs.
The working arrangements for this role are accurate as of the date of posting and may change based on project and client requirements. What you must have to be considered
8–12 years of experience in software engineering, AI/ML, or applied AI development, with strong hands-on expertise in Python-based systems
Proven experience building and deploying LLM applications , including RAG architectures and vector databases
Hands-on experience with LLM frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel, or equivalents)
Strong understanding of prompt engineering, context design, and structured outputs, including security considerations such as prompt injection mitigation
Experience developing scalable AI systems and APIs in cloud environments (AWS, Azure, or GCP)
Demonstrated experience leading engineering teams or owning modules/workstreams in a delivery environment
Strong problem-solving skills and ability to translate business requirements into technical solutions These will help you succeed
Experience integrating AI solutions with enterprise systems, identity platforms (SSO/IAM), and secure data environments
Familiarity with evaluation, observability, and monitoring tools for AI/LLM systems
Experience with MLOps practices, CI/CD pipelines, and production deployment of AI applications
Exposure to distributed systems and scalable cloud-native architectures
Experience working in consulting or client-facing environments
Advanced degree in Computer Science, Engineering, or related field is a plus Why You’ll Love This Role
You’ll have the opportunity to work on cutting-edge AI technologies, shape real-world enterprise AI solutions, and grow into broader technical leadership roles while working with a global team of experts.
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. Compensation
$130,000 – $158,500
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
Salary Context
This $130K-$158K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($144K) sits 32% below the category median. Disclosed range: $130K to $158K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Cognizant AI Hiring
Cognizant has 41 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Data Scientist, AI Safety. Positions span Tampa, FL, US, Washington, DC, Hartford, CT. Compensation range: $100K - $204K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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