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About This Role
### Who we are:
Cognizant is one of the world's leading professional services companies, transforming clients' business, operating, and technology models for the digital era. Our unique industry-based, consultative approach helps clients envision, build, and run more innovative and efficient businesses. Headquartered in the U.S., Cognizant, a member of the NASDAQ-100, is consistently listed among the most admired companies in the world. Learn how Cognizant helps clients lead with digital at www.cognizant.com.
### Who you are:
We are looking for a dynamic, results oriented, commercial leaders in AI, Analytics & Engineering field keen to be part of an ambitious culture accelerating digital change for our large, sophisticated clients. Additionally, you will have specialized familiarity and experience in the Manufacturing and Automotive sectors where we are looking to drive growth and excellence.
Service Line Sales Specialists (SLS) are field sales executives passionate about crafting, pursuing, and closing opportunities in nominated industry markets. We work closely with and within client accounts to support vertical industry growth.
### What you'll do:
The Service line sales specialist will provide deep expertise in Artificial Intelligence & Analytics (AIA) Practice supporting Cognizant’s client account teams in vertical markets and their account expansion plans into new markets and business areas. SLS’ are usually assigned into existing markets and some SLS’ will also engage in new logo markets.
The Service Line Specialist will be a “trusted advisor” to both the client and the Cognizant Client Partner blending strategic, tactical and ‘street savvy’ sales experience.
Responsibilities
- Forge relationships with VP/CXO decision makers across IT and business teams in the Manufacturing, and Automotive domains.
- Map client organization, build outstanding relationships with new business units, and build a sales strategy for developing new business opportunities
- Run end-to-end lead generation, sales and RFI/RFP processes
- Drive collective focus with multiple teams on larger multi service line deals
- Drive revenue by prioritization, structuring, and leading digital engagements
- Work closely with the practices and delivery organization to co-define and drive transformation strategy and service offerings across the areas of Software Engineering, Experience technologies, design and quality engineering.
- Own and deliver on revenue and Total Commercial Value targets
- Envision and build new opportunities within existing accounts and new logos.
- Lead financial and contract terms, negotiation, and outsourcing discussions
- Help account leadership and delivery leadership by highlighting risks and issues related to the engagements
- Develop and execute Account Growth strategy and business plans relevant to data, engineering and analytics
- Coordinate with account teams to integrate with the account’s larger growth plan
- Responsible for Sales, Business Development & Customer Relationship activities.
- Work in a matrix organization, enabling prospecting and other sales management goals
- Manage operations and maintain process and system hygiene to enable system oriented KPI’s and measurement
Qualifications and Experience:
- Sales Experience: 12 - 15 years of experience in consultative selling of data engineering, analytics and AI services services/solutions.
- Manage an overall portfolio of $25M - $50M
- Knowledge of Manufacturing and Automotive domains and demonstrated experience in this domain for 2+ years
- Demonstrated ability to manage entire sales lifecycle from opportunity identification (focus on farming) to negotiation/contracting.
- Capable of working with clients to envision, structure and specify solution requirements. Demonstrated ability to close deals ranging in size from $1M to $5M+
- Strong knowledge on current industry trends and capabilities in data engineering, AI & analytics
- Strong verbal and written communication skills. Capable of structuring and editing presentations and proposals leveraging content that is both self-generated and provided by colleagues
- Self-drive, flexibility and ownership of objectives
- Logical and structured approach to presenting opinions/views and interpretation of information
- Demonstrated ability to influence decisions makers and develop followership among colleagues and subordinates
- Good understanding of data mesh, data fabric architectures, next gen data engineering & analytics capabilities, technologies & platforms such as, Databricks, Snowflake etc.
Location:
- Preferred locations include Detroit, MI/ Minneapolis, MN(or surrounding areas).
- Hybrid work location (3 days in office)
Salary and other Compensation
The annual salary for this position is between $190,000 - $220,000 depending on the experience and other qualifications of the successful candidate. This position is also eligible for Cognizant’s discretionary annual incentive program and stock awards, based on performance and subject to the terms of Cognizant’s applicable plans.
Work Authorization:
- Should be authorized to work in the US without having dependency on short term work Visa.
Salary Context
This $190K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Cognizant Technology Solutions, 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 $154,000 based on 8,743 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $147,000. This role's midpoint ($205K) sits 33% above the category median. Disclosed range: $190K to $220K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Cognizant Technology Solutions AI Hiring
Cognizant Technology Solutions has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Detroit, MI, US, Princeton, NJ, US. Compensation range: $194K - $220K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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