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About This Role
Location: Remote
Employment Type: Full\-Time
Job ID: 00070021851
As a GenAI Solutions Architect, you will make an impact by leading AI\-first engineering initiatives and designing enterprise\-scale Generative AI solutions that accelerate innovation and business transformation. You will be a valued member of our AI \& Digital Engineering team and work collaboratively with business leaders, product teams, architects, and engineering organizations to deliver scalable, cloud\-native AI solutions that drive measurable business outcomes.
In this role, you will:
Lead the architecture, design, and implementation of enterprise Generative AI solutions across business and technology programs.
Design scalable, cloud\-native architectures leveraging Large Language Models (LLMs), AI agents, APIs, orchestration frameworks, and microservices.
Build and deploy AI\-powered applications using technologies such as GPT, Claude, Gemini, Llama, and advanced Prompt and Context Engineering techniques.
Drive AI\-assisted software engineering practices using tools such as GitHub Copilot and other AI development solutions to improve engineering productivity and accelerate delivery.
Architect and implement solutions leveraging AWS, Amazon Bedrock, and modern cloud technologies.
Establish best practices for AI platform engineering, responsible AI adoption, DevOps, and CI/CD automation.
Collaborate with stakeholders to define AI strategies, solution roadmaps, and implementation approaches.
Provide technical leadership and mentor engineering teams on AI architecture, platform engineering, and cloud\-native development.
Work model
We believe hybrid work is the way forward as we strive to provide flexibility wherever possible. Based on this role's business requirements, this is a hybrid position requiring 3 days per week onsite at a client location in Charlotte, North Carolina, or Jersey City, NJ with the remaining days worked remotely.
The working arrangements for this role are accurate as of the date of posting. This may change based on the project you're engaged in, as well as business and client requirements. Rest assured; we will always be clear about role expectations.
What you need to have to be considered
Proven experience designing and delivering Generative AI solutions in enterprise environments.
Strong expertise in LLMs, AI Agents, Prompt Engineering, and Context Engineering.
Experience building cloud\-native applications using AWS, including AI and platform services.
Experience with AI\-assisted development tools such as GitHub Copilot, Claude, or similar technologies.
Strong understanding of APIs, microservices, distributed systems, and modern software architecture patterns.
Experience with DevOps, CI/CD pipelines, and engineering automation practices.
Strong stakeholder management, communication, and leadership skills.
Experience leading architecture discussions and influencing enterprise technology strategy.
These will help you stand out
Experience with Amazon Bedrock, AI platform engineering, and agentic AI frameworks.
Knowledge of Palantir or similar enterprise AI and data platforms.
Experience implementing AI governance and responsible AI practices.
Insurance industry experience.
Experience leading enterprise AI transformation initiatives.
Experience mentoring architects and engineering teams on AI adoption and best practices.
Salary and Other Compensation:
Applicants will be accepted till 9/03/2026
Cognizant will only consider applicants for this position who are legally authorized to work in the United States without company sponsorship.
- Please note, this role is not able to offer visa transfer or sponsorship now or in the future\*
The annual salary for this position will be in the range of $119,000 \- $139,000 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
Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Our strength is built on our ability to work together. Our diverse backgrounds offer different perspectives and new ways of thinking. It encourages lively discussions, creativity, productivity, and helps us build better solutions for our clients. We want someone who thrives in this setting and is inspired to craft meaningful solutions through true collaboration.
If you are content with ambiguity, excited by change, and excel through autonomy, we’d love to hear from you!
Apply Now!
\#LI\-IK1
Salary Context
This $119K-$139K range is in the lower quartile 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($129K) sits 40% below the category median. Disclosed range: $119K to $139K.
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
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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