AI Developer Jobs
AI Developer jobs are open across technology, finance, healthcare, and government, from entry-level to staff and principal, with specializations in machine learning engineering, generative AI, and NLP. Find a role that fits from the openings below and apply directly.
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INTRODUCTION
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
BASIC 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
LOCATION
Location: Hybrid (Chicago, IL)
INTRODUCTION
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
BASIC 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
LOCATION
Location: Hybrid (Chicago, IL)
See All 182+ AI Developer Jobs
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Find AI Developer JobsAI Developer Job Market
A snapshot from current openings nationwide, updated as new roles post.
Who's Hiring
- NVIDIA21
- Tata Consultancy Services (TCS)10
- Citi7
- Synechron7
- Capgemini6
Top Industries Hiring
- Technology & Software98
- Consulting & Professional Services18
- Electronics & Hardware17
- Law & Legal Services12
- Banking & Financial Services8
What Employers Look For
The qualifications that appear most often in AI developer jobs.
- Proficiency in Python and at least one deep learning framework such as PyTorch or TensorFlow
- Experience building, training, and deploying machine learning or large language models in production
- Familiarity with cloud platforms such as AWS, Google Cloud, or Azure for model serving and MLOps
- Strong understanding of data preprocessing, feature engineering, and model evaluation techniques
- Bachelor's or master's degree in computer science, data science, or a related quantitative field
- Experience with version control, experiment tracking tools, and containerization using Docker or Kubernetes
Tips for Your AI Developer Job Search
Quantify model performance on your resume
Recruiters screening ai developer resumes want to see concrete outcomes, not just tools. Replace 'built a recommendation model' with metrics like latency reduction, accuracy improvement, or throughput gains. Numbers tied to business impact clear screening filters faster than capability lists.
Target openings by framework, not just title
Many ai developer postings bury the real requirement in the skills section. Search by the framework you know best, PyTorch or JAX or LangChain, alongside the job title. You'll surface roles that match your stack before you read a single job description.
Apply early to roles that fit
Migrate Mate lists ai developer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Build a public artifact for each major project
Hiring managers for ai developer roles increasingly expect a link they can click, a GitHub repo, a model card, a published demo, not just a bullet point. Make sure every significant project in your resume maps to something publicly reviewable before you start applying.
Prepare to explain tradeoffs, not just solutions
Technical interviews for ai developers frequently go off-script into architecture decisions, why you chose one embedding strategy over another, or how you handled data drift. Rehearse explaining the tradeoff you made on a past project, not just the outcome you reached.
Negotiate on compute access and tooling budget
Beyond base pay, ai developer roles vary widely on cloud compute credits, access to proprietary datasets, and hardware allocation. Ask directly what the team's GPU budget is and how model experiments are provisioned. This affects your day-to-day productivity more than most perks.
AI Developer Jobs: Frequently Asked Questions
Which companies are hiring the most ai developers?
The companies hiring the most ai developers right now include NVIDIA, Tata Consultancy Services (TCS), and Citi, with the largest share of openings in California, Texas, and New York, based on current listings on Migrate Mate as of June 2026. Demand is concentrated in technology, defense, and enterprise software, though healthcare and financial services have expanded hiring significantly.
How many ai developer jobs are remote?
About 30% of ai developer openings are fully remote or hybrid as of June 2026, making it one of the more distributed engineering roles in the market. Positions focused on model research, fine-tuning, and API integration tend to be the most remote-friendly, while roles requiring on-site GPU clusters or regulated data environments are more likely to require in-person work.
How do you become an ai developer?
Start by building a strong foundation in Python and linear algebra, then move into a deep learning framework like PyTorch or TensorFlow through structured projects. Work through publicly available datasets to train and evaluate your own models, document the results in a GitHub portfolio, and pursue roles at the junior level where you can work alongside senior practitioners. Hands-on project experience consistently matters more than credentials alone in this field.
Can you get hired as an ai developer with little or no experience?
Yes, but you need to substitute experience with demonstrable output. Build two or three end-to-end projects, a fine-tuned language model, a computer vision classifier, or a retrieval-augmented generation pipeline, and make them publicly accessible. Apply to companies with dedicated ML infrastructure teams where junior developers support senior researchers, and target roles titled ML engineer or AI engineer, which often have more structured onboarding than pure research positions.
What does the ai developer interview process look like?
Most ai developer interview processes include an initial recruiter screen, a take-home or live coding assessment focused on Python and data manipulation, a technical round covering ML fundamentals and model design, and a system design interview where you architect an end-to-end ML pipeline. Senior roles often add a research or case study round where you critique an existing model or propose an approach to a novel problem. The full process typically runs across multiple weeks.
Where can I find and apply to ai developer jobs?
You can find and apply to ai developer jobs on Migrate Mate, which lists current openings from companies across the United States. Search the listings to find roles that match your stack and experience level, then apply directly to each one that fits. No intermediary steps are required between finding a listing and submitting your application.
See All 182+ AI Developer Jobs
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