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Leading at Cognizant
This is a Leadership (D\+) role at Cognizant. We believe how you lead is as important as what you deliver. Cognizant leaders at every level: Drive our business strategy and inspire teams around our future. Live the leadership behaviors , leading themselves, others and the business. Uphold our Values , role modeling them in every action and decision. Nurture our people and culture , creating a workplace where all can thrive.
At Cognizant, leadership transcends titles and is embodied in actions and behaviors. We empower our leaders at every level to drive business strategy, inspire teams, uphold our values, and foster an inclusive culture.
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 Associate Partner, Consulting, Autonomous Telecommunications and Agentic AI , you will make an impact by defining and commercializing AI\-first strategies that help telecommunications clients reduce costs, improve customer experiences, and modernize their operations. You will be a valued member of the Telecommunications Consulting team and work collaboratively with clients, sales leaders, client partners, consulting teams, technology specialists, analysts, and strategic partners.
This role combines telecommunications industry expertise, AI\-enabled offering development, executive client engagement, and market leadership. You will shape differentiated solutions across customer experience, telecommunications products, sales and marketing, business and operations support systems, service operations, and networks.
In this role, you will:
Define and execute AI\-first telecommunications strategies that translate emerging capabilities into measurable client outcomes, including cost reduction, improved customer experience, operational efficiency, and new growth opportunities.
Develop and commercialize differentiated autonomous telecommunications offerings , including agentic AI, multi\-agent systems, closed\-loop operations, zero\-touch operations, AI\-enabled customer experience, telecommunications products, sales and marketing, business and operations support systems, and networks.
Lead strategic client engagements and large\-scale transformation programs , building trusted relationships with senior executives, shaping tailored solutions, establishing executive governance, and advancing commercial opportunities.
Scale domain\-led, AI\-augmented delivery models , including agentic pods and digital coworkers, to modernize legacy environments and generate non\-linear improvements in productivity, quality, and efficiency.
Strengthen Cognizant’s market position and ecosystem , developing the offering roadmap, producing thought leadership, engaging external analysts and industry forums, enabling internal sales teams, and establishing strategic alliances and partnerships.
Consistently demonstrate the Cognizant Way to Lead , which means operating with Personal Leadership by building trust, collaboration, and inclusion; Organizational Leadership by driving vision and purpose, demonstrating a strategic and enterprise mindset, and communicating a bold direction that inspires purpose; and Business Leadership by 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 hybrid position based in Texas with up to 25% travel to client and Cognizant locations . Regardless of your working arrangement, we are here to support a healthy work\-life balance through our various wellbeing programs.
The working arrangements for this role are accurate as of the date of posting. This may change based on the project you’re engaged in, as well as business and client requirements. Rest assured, we will always be clear about role expectations.
What you must have to be considered
Extensive leadership experience in telecommunications consulting, industry advisory, offering development, or large\-scale business and technology transformation.
Deep expertise across telecommunications customer experience, products, sales and marketing, business and operations support systems, service operations, and network domains.
Demonstrated experience defining AI\-first strategies and developing solutions involving agentic AI, multi\-agent systems, closed\-loop operations, or zero\-touch operations.
Proven ability to build senior executive relationships, influence C\-suite stakeholders, lead executive governance, and shape or lead complex transformation programs.
Strong commercial and market\-development capabilities, including offering strategy, portfolio roadmaps, thought leadership, analyst engagement, strategic partnerships, and sales enablement.
These will help you succeed
Experience modernizing complex or legacy telecommunications environments through automation, data, cloud, and AI\-enabled operating models.
Ability to connect emerging technology developments with practical business outcomes, differentiated client solutions, and commercial opportunities.
A recognized telecommunications industry presence through publications, analyst relationships, industry forums, partnerships, or executive networks.
Experience scaling AI\-augmented delivery models, digital coworker solutions, or multidisciplinary consulting teams.
Embodiment of the Cognizant Way to Lead : Leading Self, Leading Others, and Leading the Business, along with Cognizant’s values of Work as One, Dare to Innovate, Raise the Bar, Do the Right Thing, and Own It .
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.
Benefits
Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:
Medical, dental, vision, and life insurance
401(k) plan and contributions
Employee stock purchase plan
Employee assistance program
10 paid holidays plus paid time off
Paid parental leave and fertility assistance
Learning and development certifications and programs
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 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 $214,900 based on 6,420 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,000.
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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