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About This Role
AI/ML Engineer – Generative AI Platforms (Remote)
About Cognizant
Cognizant is one of the world's leading professional services companies, helping clients modernize technology, reimagine processes, and transform experiences. We engineer modern businesses to improve everyday life. Our associates around the globe help clients build stronger, more agile, and innovative organizations.
Our AI and Digital Engineering practice helps clients unlock value through Artificial Intelligence, Machine Learning, Intelligent Automation, Cloud Transformation, and Data Modernization. We partner with global enterprises to build scalable AI-powered solutions that accelerate innovation, improve productivity, and drive business outcomes.
Role Overview
Cognizant is seeking an experienced AI/ML Engineer – Generative AI Platforms to join our growing AI Engineering team.
This is a hands-on engineering role focused on designing, developing, deploying, and supporting AI-powered Document Processing and AI Authoring platforms. The ideal candidate will have deep experience building enterprise Generative AI solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Intelligent Document Processing (IDP), semantic search, and cloud-native AI architectures.
You will collaborate with Solution Architects, Product Owners, Data Engineers, and Software Engineering teams to develop innovative AI solutions that deliver scalable, secure, and production-ready business capabilities.
Work Model
This position is fully remote within the United States, providing the flexibility to work from home while collaborating with global teams across multiple locations and time zones.
Occasional travel may be required for client meetings, planning sessions, team collaboration events, and strategic initiatives.
Key Responsibilities
Generative AI Solution Development
- Design, develop, and deploy enterprise-grade Generative AI solutions using Large Language Models (LLMs).
- Build intelligent document processing and AI-assisted authoring capabilities supporting complex business workflows.
- Develop and optimize Retrieval-Augmented Generation (RAG) architectures to enhance response quality and contextual relevance.
- Implement prompt engineering strategies to improve AI performance, accuracy, and reliability.
- Collaborate with product and business teams to translate business requirements into scalable AI solutions.
Document Intelligence & AI Authoring
- Develop capabilities for: + Document Extraction + Document Classification + Data Validation + Knowledge Retrieval + AI-Assisted Content Generation
- Design workflows that automate document understanding and content creation processes.
- Build validation frameworks that improve accuracy, traceability, and confidence in AI-generated outputs.
- Support end-to-end lifecycle management of AI-powered document solutions.
RAG, Semantic Search & Knowledge Retrieval
- Design and optimize embedding strategies for enterprise data and document repositories.
- Implement vector search and semantic retrieval solutions to improve knowledge discovery.
- Develop grounding mechanisms that ensure AI responses are accurate, contextual, and explainable.
- Build and maintain retrieval pipelines that support high-performance search and content generation capabilities.
- Evaluate and enhance retrieval effectiveness using modern AI evaluation methodologies.
API & Microservices Development
- Design and develop APIs and microservices using Python and FastAPI.
- Build scalable and reusable backend services supporting AI applications and integrations.
- Develop secure API-based connections between AI platforms, enterprise systems, and third-party applications.
- Support microservice deployment, monitoring, maintenance, and optimization activities.
AWS Cloud & AI Platform Engineering
- Build and support cloud-native AI solutions leveraging AWS services.
- Develop and maintain solutions utilizing: + AWS Bedrock + AWS Lambda + Amazon S3 + Amazon RDS + Related AWS services
- Implement scalable, secure, and highly available cloud architectures for AI workloads.
- Collaborate with cloud and platform engineering teams to optimize performance and cost efficiency.
Testing, Quality & Operational Excellence
- Create and execute: + Functional Tests + Integration Tests + AI Quality Validation Tests + Performance Tests
- Support AI model evaluation, monitoring, and continuous improvement initiatives.
- Troubleshoot production issues and implement long-term solutions.
- Contribute to quality engineering processes and automated testing frameworks.
Security, Governance & Responsible AI
- Ensure solutions comply with enterprise security standards and governance requirements.
- Implement Responsible AI practices that support explainability, transparency, and risk management.
- Adhere to data privacy, compliance, and information security requirements.
- Support auditability, monitoring, and governance controls within AI platforms.
Required Qualifications
- Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related field, or equivalent work experience.
- Strong hands-on experience developing enterprise AI/ML solutions.
- Expertise in: + Python + FastAPI + REST APIs
- Experience with: + Large Language Models (LLMs) + Generative AI + Prompt Engineering + Retrieval-Augmented Generation (RAG)
- Strong understanding of: + Embeddings + Vector Databases + Semantic Search + Knowledge Retrieval
- Hands-on experience with: + AWS Bedrock + AWS Lambda + Amazon S3 + Amazon RDS
- Experience with: + PostgreSQL + Git + Docker + CI/CD Pipelines
- Strong analytical, troubleshooting, and problem-solving skills.
- Excellent communication and collaboration skills.
Preferred Qualifications
- Experience building enterprise Generative AI applications.
- Experience with Intelligent Document Processing (IDP) platforms and solutions.
- Expertise in: + Semantic Search + Knowledge Retrieval Systems + AI Authoring Applications
- Experience with: + LangChain + LangGraph
- Knowledge of: + AI Evaluation Frameworks + Model Monitoring + AI Governance
- Experience working within Life Sciences, Pharmaceutical, Healthcare, or other regulated industries.
- Familiarity with cloud-native architecture patterns and microservices frameworks.
Technical Expertise
Candidates should demonstrate experience in:
- Generative AI
- Large Language Models (LLMs)
- Prompt Engineering
- Retrieval-Augmented Generation (RAG)
- Intelligent Document Processing (IDP)
- Semantic Search
- Knowledge Retrieval
- Vector Databases
- PGVector
- Python Development
- FastAPI
- REST API Design
- AWS Bedrock
- AWS Lambda
- Amazon S3
- Amazon RDS
- PostgreSQL
- Docker
- Git
- CI/CD
- LangChain
- LangGraph
- Responsible AI
Salary and Other Compensation
The annual salary for this position is anticipated to be between $120,000 and $145,000, depending on experience, qualifications, geographic location, skills, and other factors permitted by law.
In addition to base salary, this position may be eligible for:
- Annual performance-based bonus
- Merit-based compensation increases
- Employee referral bonuses, where applicable
The compensation information provided is accurate as of the date of this posting. Cognizant reserves the right to modify compensation information based on business needs and applicable laws.
Benefits
Cognizant offers a comprehensive benefits package, including:
- Medical, Dental, and Vision Coverage
- Prescription Drug Coverage
- Life and Disability Insurance
- Employee Assistance Program (EAP)
- Paid Vacation and Company Holidays
- Retirement Savings Programs (subject to eligibility)
- Parental and Family Support Benefits
- Employee Wellness Programs
- Tuition Reimbursement and Professional Development Opportunities
- Access to Training, Certifications, and Career Development Programs
Benefits are subject to eligibility requirements and applicable provincial regulations.
Why Cognizant
- Help shape the future of enterprise AI and Generative AI solutions.
- Develop cutting-edge AI-powered document processing and authoring platforms.
- Work with leading cloud, AI, and data technologies.
- Collaborate with experienced architects, engineers, and data scientists across global teams.
- Gain exposure to large-scale enterprise AI transformation initiatives.
- Access ongoing learning, certification, and career advancement opportunities.
Equal Opportunity Employer
Cognizant is an equal opportunity employer. We are committed to fostering an inclusive workplace where all associates feel valued, respected, and empowered to contribute their unique perspectives. All qualified applicants will receive consideration for employment without regard to race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity or expression, age, disability, marital status, family status, veteran status, or any other characteristic protected by applicable law.
Applications will be accepted until the position is filled.
Salary Context
This $120K-$145K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($132K) sits 37% below the category median. Disclosed range: $120K to $145K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Cognizant AI Hiring
Cognizant has 41 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Data Scientist, AI Safety. Positions span Tampa, FL, US, Washington, DC, Hartford, CT. Compensation range: $100K - $204K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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