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About This Role
Senior Developer – AI/ML \& Generative AI (Airflow, Python, MLOps)
Location: Hybrid (Chicago, IL)
Experience: 6–10\+ years About Cognizant
Cognizant is a leading global professional services company, helping organizations modernize technology, reimagine processes, and transform customer experiences. We leverage expertise in AI, cloud, and digital engineering to deliver measurable outcomes across industries. Role Overview
We are seeking a Senior Developer with strong expertise in data engineering, AI/ML, and Generative AI to build scalable, production\-grade solutions.
This role focuses on developing Airflow\-orchestrated pipelines, operationalizing machine learning models, and building reusable AI frameworks that drive smarter business decisions and deliver meaningful impact. Key Responsibilities Data Pipelines \& Workflow Orchestration
Design and implement robust data and model pipelines using Apache Airflow
Orchestrate complex workflows ensuring reliability, scalability, and performance across hybrid environments AI \& Generative AI Development
Develop Generative AI services to transform enterprise data into insights and content
Ensure solutions meet accuracy, safety, and compliance standards
Apply prompt engineering and model integration techniques in business applications ML Engineering \& Productionization
Build production\-ready machine learning solutions from experimental prototypes
Develop scalable services with monitoring, logging, and fault tolerance
Ensure consistent delivery of value to downstream systems MLOps \& Automation
Implement MLOps best practices , including automated training, evaluation, deployment, and rollback
Enhance development velocity through CI/CD automation and lifecycle management Data Engineering \& Feature Management
Build and maintain feature pipelines and data preparation processes
Ensure high\-quality, governed, and traceable data for model training and inference Observability \& Reliability
Implement monitoring, logging, and alerting for pipelines and models
Detect and resolve issues related to data quality, model drift, and system performance Collaboration \& Delivery
Partner with data scientists, engineers, and product stakeholders to translate ideas into scalable solutions
Contribute to backlog prioritization and iteration planning Standards, Governance \& Best Practices
Ensure security, privacy, and responsible AI principles are embedded in solutions
Promote best practices in version control, testing, and CI/CD Innovation \& Continuous Improvement
Evaluate emerging tools in Generative AI and MLOps
Support experimentation through A/B testing and model comparison frameworks Mentorship \& Leadership
Mentor junior developers on Airflow, debugging, and deployment strategies
Foster a collaborative and growth\-oriented team environment Required Qualifications
Strong experience in Python\-based development for data and AI applications
Hands\-on expertise with Apache Airflow (DAG design, operators, scheduling optimization)
Solid understanding of machine learning concepts (supervised/unsupervised learning, evaluation, feature engineering)
Practical experience with MLOps practices (packaging, deployment, monitoring, lifecycle management)
Experience implementing Generative AI solutions (prompt design, model integration)
Strong SQL and data modeling skills for high\-performance data pipelines Preferred Qualifications
Experience with containerization (Docker, Kubernetes)
Exposure to cloud platforms (AWS, Azure, or GCP)
Familiarity with CI/CD pipelines and DevOps practices
Knowledge of Responsible AI frameworks and governance models
Experience working in enterprise\-scale, hybrid environments
Strong communication and stakeholder management skills Certifications (Preferred)
TensorFlow Developer Certification
AWS Certified Machine Learning – Specialty
Equivalent certifications in AI/ML or MLOps Compensation
Base Salary Range: $120,000 – $130,000 per year
Final compensation is based on experience, skills, and location
Eligible for performance\-based bonuses and incentive compensation Benefits
Cognizant offers a comprehensive benefits package to support your well\-being and career growth:
Health \& Wellness: Medical, dental, and vision insurance
Financial Benefits: 401(k) with company match, life and disability insurance
Time Off: Generous PTO, paid holidays, and parental leave
Learning \& Development: Access to certifications, training programs, and career advancement opportunities
Flexible Work: Hybrid work model to support work\-life balance
Employee Experience: Wellness programs, employee resource groups, and recognition initiatives Why Join Cognizant?
Work on cutting\-edge Generative AI and ML solutions
Collaborate with global clients and cross\-functional teams
Grow your career in a high\-demand, innovation\-driven space
Be part of a company committed to inclusion, diversity, and impact
Salary Context
This $120K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($125K) sits 32% below the category median. Disclosed range: $120K to $130K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Cognizant AI Hiring
Cognizant has 16 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Research Engineer, Research Scientist. Positions span Seattle, WA, US, Plano, TX, US, Rockville, MD, US. Compensation range: $84K - $218K.
Location Context
AI roles in Chicago pay a median of $200,100 across 329 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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