Interested in this AI/ML Engineer role at Cognizant?
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About This Role
We are seeking a GenAI Engineer to design, build, and deploy generative AI solutions that support enterprise applications, automation, knowledge discovery, and intelligent user experiences. The ideal candidate will have hands\-on experience working with large language models, prompt engineering, retrieval\-augmented generation, model evaluation, and application integration. Experience with Azure DevOps is helpful for supporting deployments, CI/CD workflows, and collaboration with engineering teams, though this role is not primarily DevOps\-focused.
Client location choices\-\-Middletown NJ, Atlanta GA, Dallas TX
Key Responsibilities • Design, develop, and integrate GenAI\-powered applications using large language models and related AI services • Build solutions involving prompt engineering, retrieval\-augmented generation, embeddings, vector search, and AI agents • Develop APIs, services, and application components that connect GenAI capabilities to business workflows • Work with structured and unstructured data to support knowledge retrieval, summarization, classification, and content generation use cases • Evaluate model performance, response quality, hallucination risk, and reliability across different scenarios • Collaborate with product, data, engineering, and architecture teams to deliver scalable AI solutions • Implement responsible AI practices including security, privacy, monitoring, explainability, and governance considerations • Support deployment and release processes using tools such as Azure DevOps, Git, CI/CD pipelines, and cloud environments • Create documentation, reusable patterns, and technical guidance for GenAI solution development • Stay current with emerging GenAI frameworks, model capabilities, and best practices
Required Qualifications • 3\+ years of software engineering, data engineering, machine learning, or AI application development experience • Hands\-on experience building applications with large language models • Experience with prompt engineering, RAG architectures, embeddings, and vector databases • Proficiency in Python and familiarity with APIs, microservices, or backend development • Experience working with cloud\-based AI services, preferably on Azure • Understanding of model evaluation, testing, monitoring, and quality improvement techniques • Familiarity with secure development practices and enterprise application integration • Strong problem\-solving, communication, and collaboration skills
Preferred Qualifications • Experience with Azure OpenAI Service, Azure AI Search, Azure Machine Learning, or related Azure AI services • Working knowledge of Azure DevOps, including repositories, boards, pipelines, and release workflows • Experience with Agentic\-LLM solutions using LangChain, LangGraph, or similar tools • Experience with vector databases or search platforms such as Azure AI Search • Familiarity with containerization, CI/CD, and deployment automation • Experience supporting enterprise\-scale AI solutions, chatbot platforms, knowledge assistants, or intelligent automation use case
About Cognizant’s IoT Practice:
Intelligent, IoT\-enabled products will soon result in the proliferation of data and disrupt virtually all industries. To be successful, both large and small companies must leverage IoT capabilities by designing modern products that fundamentally connect people with processes. Within Cognizant IOT, we engineer industry\-aligned, IoT\-enabled products that merge industry needs with human drivers. Our intelligent products will revolutionize experiences and result in exciting, transformative outcomes. Without human\-centered thinking, connected products are just standalone things—but with it, our modern connected products facilitate a unified way of life enjoyed by all.
- Please note, this role is not able to offer visa transfer or sponsorship now or in the future\*
Applications will be accepted until July 31st , 2026
LI\#\- \#LI\-SC6
Compensation \& Benefits
$74,500 – $117,000 per year
Bonus \+ Comprehensive Benefits
Additional Information
Onsite role
- ️ Visa sponsorship is not available now or in the future. Candidates requiring sponsorship will not be considered.
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
Salary Context
This $74K-$117K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($95K) sits 56% below the category median. Disclosed range: $74K to $117K.
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
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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
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