Interested in this AI/ML Engineer role at Cognizant?
Apply Now →Skills & Technologies
About This Role
Position Overview
Life sciences organizations are under pressure to innovate their processes as fast as possible. Thirty of the top global pharmaceutical companies work with Cognizant. We are redefining the way life sciences companies do digital and we are offering solutions specific to the challenges of these companies.
We are seeking a motivated and curious Entry\-Level Engineer with a strong foundation in Python, data analytics, modeling, and Generative AI . This role is ideal for recent graduates or early\-career professionals who are eager to apply analytical thinking and modern AI techniques to solve real\-world business and engineering problems.
You will work closely with Life Sciences commercial cross\-functional teams across Sales, Marketing and Market Access to analyze data, build models, and contribute to AI\-driven solutions that support decision\-making, automation, and innovation.
Key Responsibilities
Develop and maintain Python\-based scripts and applications for data analysis and modeling
Collect, clean, and analyze structured and unstructured data from multiple sources
Build and evaluate statistical, predictive, or machine learning models
Assist in the development and experimentation of Generative AI solutions (e.g., LLM\-based applications, prompt engineering, embeddings)
Create data visualizations and dashboards to communicate insights effectively
Collaborate with senior engineers, data scientists, and business stakeholders
Document code, models, and analytical findings clearly and thoroughly
Stay current with emerging trends in data analytics, AI, and Generative AI technologies
Hands\-on experience working with real\-world data and AI solutions
Mentorship from experienced engineers and data scientists
Opportunities to grow skills in Generative AI and advanced analytics
Collaborative and learning\-focused work environment
Required Qualifications
Bachelor’s degree in Engineering, Computer Science, Data Science, Mathematics, Statistics, or a related field
Strong proficiency in Python (e.g., NumPy, Pandas, Matplotlib/Seaborn)
Understanding of data analytics concepts, including data cleaning, exploratory analysis, and basic statistics
Exposure to modeling techniques such as regression, classification, or time\-series analysis
Familiarity with Generative AI concepts, such as:
Large Language Models (LLMs)
Prompt engineering
APIs for GenAI platforms (e.g., OpenAI, Azure OpenAI, Hugging Face)
Basic knowledge of SQL and working with databases
Strong problem\-solving, analytical, and communication skills
Preferred / Nice\-to\-Have Skills
Experience with machine learning libraries (e.g., scikit\-learn, TensorFlow, PyTorch)
Hands\-on experience through internships, academic projects, or personal projects
Familiarity with cloud platforms (Azure, AWS, or GCP)
Understanding of MLOps or model deployment concepts
Experience using Git or other version control systems
Exposure to data visualization tools (Power BI, Tableau, or similar)
Life Sciences Commercial experience / understanding of modeling techniques
Location(s)
New hires will be deployed to Cognizant office in Bridgewater, NJ (and willing to relocate) where you will work alongside other experienced Cognizant associates delivering technology solutions. Applicants must be willing to relocate to these major geographic area.
Start Date(s)
New hires will start in August 2026 . While we will attempt to honor candidate start date preferences, business need and position availability will determine final start date assignment of June or August. Exact start dates will be communicated with enough time for you to plan effectively.
Why Choose Us?
Cognizant delivers solutions that draw upon the full power and scale of our associates. You will be supported by high\-caliber experts and employ some of the most advanced and patented capabilities. Our associate’s diverse backgrounds offer varied perspectives and fuel new ways of thinking. We encourage lively discussions which inspire better results for our clients. If you’re comfortable with ambiguity, excited by change, and excel through autonomy, we’d love to hear from you.
Salary and Other Compensation:
Applications are accepted on an ongoing basis. The annual salary for this position is $65,000\.00 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 the following benefits for this position, subject to applicable eligibility requirements:
Medical/Dental/Vision/Life Insurance
Paid holidays plus Paid Time Off
401(k) plan and contributions
Long\-term/Short\-term Disability
Paid Parental Leave
Employee Stock Purchase Plan
Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Work Authorization
Due to the nature of this position, Cognizant cannot provide sponsorship for U.S. work authorization (including participation in a CPT/OPT program) for this role.
Cognizant is always looking for top talent. We are searching for candidates to fill future needs within the business. This job posting represents potential future employment opportunities with Cognizant. Although the position is not currently available, we want to provide you with the opportunity to express your interest in future employment opportunities with Cognizant. If a job opportunity that you may be qualified for becomes available in the future, we will notify you. At that time you can determine whether you would like to apply for the specific open position. Thank you for your interest in Cognizant career opportunities.
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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $120,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Cognizant AI Hiring
Cognizant has 22 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Architect. Positions span Irving, TX, US, Louisville, KY, US, New York, NY, US. Compensation range: $85K - $435K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.