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About This Role
Practice \- AIA \- Artificial Intelligence and Analytics
About AI \& Analytics: Artificial intelligence (AI) and the data it collects and analyzes will soon sit at the core of all intelligent, human\-centric businesses. By decoding customer needs, preferences, and behaviors, our clients can understand exactly what services, products, and experiences their consumers need. Within AI \& Analytics, we work to design the future—a future in which trial\-and\-error business decisions have been replaced by informed choices and data\-supported strategies.
By applying AI and data science, we help leading companies to prototype, refine, validate, and scale their AI and analytics products and delivery models. Cognizant’s AIA practice takes insights that are buried in data and provides businesses a clear way to transform how they source, interpret and consume their information. Our clients need flexible data structures and a streamlined data architecture that quickly turns data resources into informative, meaningful intelligence.
- Please note, this role is not able to offer visa transfer or sponsorship now or in the future\*
Job Summary
We are seeking a Lead ML Engineer to drive the design, development, and deployment of advanced machine learning and AI solutions leveraging Azure OpenAI, Azure Machine Learning, Snowflake, and Python. This role will serve as the technical leader for enterprise data science initiatives, owning model architecture, ML solution design, and AI platform integration. The ideal candidate will combine deep expertise in machine learning, cloud\-native AI services, and software engineering with the ability to mentor teams and translate business challenges into scalable AI solutions. This position requires a strong balance of hands\-on engineering, technical leadership, and stakeholder engagement.
In this role, you will:
- Lead the end\-to\-end design, architecture, and implementation of machine learning and AI solutions using Azure OpenAI Services, Azure Machine Learning, and Python.
- Design and develop predictive, generative AI, and recommendation models to improve operational efficiency, customer experience, and business outcomes.
- Build and optimize machine learning pipelines including feature engineering, model training, validation, deployment, monitoring, and retraining.
- Integrate Azure OpenAI capabilities such as natural language processing, conversational AI, and generative AI into enterprise applications and workflows.
- Analyze large\-scale structured and unstructured datasets to identify opportunities for business optimization, personalization, forecasting, and automation.
- Collaborate with product owners, data engineers, architects, and business stakeholders to define AI use cases and implementation roadmaps.
- Lead technical reviews, mentor data scientists and machine learning engineers, and establish engineering best practices for model development and deployment.
- Design scalable and secure MLOps workflows leveraging GitHub, Docker, Azure Machine Learning, and cloud\-native deployment patterns.
- Define model evaluation frameworks, responsible AI controls, governance standards, and risk mitigation processes.
- Evaluate emerging AI technologies, frameworks, and Azure capabilities to drive innovation and continuous improvement.
- Communicate technical findings and business impact to executive and non\-technical stakeholders through compelling storytelling and data\-driven insights.
- Ensure AI solutions comply with enterprise security, regulatory, privacy, and governance requirements.
What you need to have to be considered
- 8\+ years of experience in Machine Learning, Data Science, Artificial Intelligence, or Advanced Analytics roles, with experience leading enterprise AI initiatives.
- Strong expertise in Python and machine learning libraries including Pandas, Scikit\-learn, XGBoost, LightGBM, and PyTorch.
- Hands\-on experience developing and deploying machine learning models in Azure Machine Learning environments.
- Experience designing and implementing Generative AI and Azure OpenAI solutions for enterprise use cases.
- Strong proficiency in SQL and experience working with Snowflake for analytics and machine learning workloads.
- Experience with model development lifecycle management, MLOps practices, model monitoring, and deployment automation.
- Proficiency with GitHub, Docker, CI/CD pipelines, and modern software engineering practices.
- Strong understanding of machine learning algorithms, feature engineering, model evaluation, recommendation systems, and predictive analytics.
- Experience working with large\-scale structured and unstructured datasets in cloud\-based environments.
- Excellent stakeholder management, communication, mentoring, and leadership skills.
- Experience in customer services, retail, utilities, or highly regulated industries is preferred.
- Familiarity with responsible AI, model governance, explainability, and enterprise AI risk management frameworks is highly desirable.
\#LI\-EF1
\#CB
\#Ind123
Applications will be accepted until 12 Aug 2026\.
Salary and Other Compensation:
The annual salary for this position is between $\[137,500 \- 161,500] 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
Salary Context
This $137K-$161K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($149K) sits 30% below the category median. Disclosed range: $137K to $161K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Cognizant AI Hiring
Cognizant has 24 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, AI Architect, AI Agent Developer. Positions span Juno Beach, FL, US, Pleasanton, CA, US, Rockville, MD, US. Compensation range: $99K - $405K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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