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About This Role
Solution Architect – AI Document Processing & Authoring (Remote)
Location: United States (Remote)
Employment Type: Full-Time
About Cognizant
Cognizant (NASDAQ: CTSH) 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 teams help clients accelerate innovation through Generative AI, Intelligent Automation, Cloud Platforms, and Data-Driven Solutions. We partner with global enterprises to design, implement, and scale AI-powered platforms that improve operational efficiency, enhance user experiences, and unlock business value.
Role Overview
Cognizant is seeking a highly skilled Solution Architect – AI Document Processing & Authoring to lead the design, implementation, and support of AI-powered document extraction and authoring solutions within enterprise platforms.
This hands-on role serves as both a solution architect and implementation expert, responsible for translating business requirements into scalable, secure, and production-ready AI solutions. The successful candidate will work across the full delivery lifecycle, including discovery, architecture, platform configuration, integration design, testing, deployment, and operational support.
The ideal candidate will possess expertise in AI-enabled document processing, enterprise integrations, prompt engineering, workflow design, and modern cloud-based architectures while collaborating with business stakeholders, product teams, developers, and platform engineering groups.
Work Model
This position is fully remote within the United States, with flexibility to collaborate across multiple geographic regions and time zones.
Occasional travel may be required for client workshops, stakeholder meetings, project planning sessions, or strategic initiatives.
Key Responsibilities
AI Solution Architecture & Design
- Lead discovery workshops and solution design sessions to identify business requirements and define AI-powered document processing and authoring solutions.
- Translate business needs into functional and technical requirements, user stories, process flows, acceptance criteria, edge cases, and implementation plans.
- Design scalable, secure, and maintainable AI-based workflows supporting document extraction, content creation, and enterprise content management.
- Define end-to-end solution architectures that align with business objectives, enterprise standards, and security requirements.
AI Document Processing Implementation
- Design and configure document-ingestion workflows, extraction schemas, validation rules, confidence thresholds, provenance requirements, and downstream processing capabilities.
- Develop and optimize document extraction workflows that improve automation, traceability, and operational efficiency.
- Configure human-in-the-loop review processes to ensure extraction quality and accuracy.
- Define data mapping and export requirements supporting enterprise reporting, analytics, and downstream integrations.
AI Authoring Solutions
- Design and implement template-driven AI authoring workflows.
- Configure document generation processes that incorporate source documentation, context management, retrieval mechanisms, and review cycles.
- Define prompt engineering strategies, citation requirements, traceability controls, and content governance standards.
- Implement workflows supporting version control, approval management, and content lifecycle management.
- Ensure generated outputs meet quality, consistency, compliance, and user-experience expectations.
Prompt Engineering & AI Optimization
- Utilize prompt engineering techniques to enhance document extraction and AI-generated content quality.
- Develop structured evaluation approaches for validating AI outputs against business requirements and quality standards.
- Perform testing and optimization of prompts, model responses, retrieval strategies, and grounding techniques.
- Partner with business stakeholders to continuously improve AI-produced outputs.
Integration Architecture
- Design and oversee implementation of APIs, automation workflows, and integration patterns connecting AI platforms with enterprise applications.
- Define data mappings and information exchange processes between content repositories, storage platforms, identity services, and downstream applications.
- Ensure reliable and secure integration of AI capabilities into existing enterprise ecosystems.
- Support implementation of automation and orchestration frameworks that improve scalability and maintainability.
Delivery & Cross-Functional Collaboration
- Partner with business SMEs, product owners, engineering teams, data teams, and platform stakeholders throughout the solution lifecycle.
- Support development teams by providing technical guidance and architectural oversight.
- Collaborate with distributed teams across the United States and global delivery centers.
- Manage project dependencies, risks, milestones, and stakeholder communications.
- Participate in architecture reviews, solution governance, and release planning activities.
Testing, Validation & Quality Assurance
- Define and execute testing strategies covering: + Functional Testing + Integration Testing + User Acceptance Testing (UAT) + AI Output Validation + Prompt Performance Testing + Failure Scenario Testing
- Facilitate defect triage activities and validate corrective actions.
- Ensure solutions meet performance, security, reliability, and usability requirements before deployment.
Production Support & Continuous Improvement
- Support production operations through monitoring, incident management, root cause analysis, and issue resolution.
- Develop runbooks, support models, operational procedures, and knowledge transfer materials.
- Coordinate release planning, deployment readiness reviews, and post-production support activities.
- Drive continuous improvement initiatives to enhance platform performance, adoption, and user experience.
Security, Compliance & Governance
- Partner with security, privacy, compliance, and governance teams to ensure solutions adhere to enterprise standards.
- Define requirements for: + Authentication + Authorization + Access Control + Auditability + Data Retention + Information Security
- Ensure AI solutions are implemented responsibly, securely, and in accordance with organizational policies.
Required Qualifications
- Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related field, or equivalent work experience.
- 10+ years of experience in solution architecture, enterprise application delivery, or platform implementation.
- Experience designing and implementing AI-powered document processing and content generation solutions.
- Strong understanding of enterprise software architecture, cloud technologies, APIs, and systems integration.
- Experience translating business requirements into technical specifications, workflows, and solution designs.
- Strong knowledge of: + Generative AI + Prompt Engineering + Retrieval-Augmented Generation (RAG) + AI Output Validation + Enterprise Content Management
- Experience supporting full software development lifecycle activities including design, testing, deployment, and production support.
- Strong analytical, troubleshooting, and problem-solving skills.
- Excellent communication, stakeholder management, and presentation skills.
- Ability to work effectively across business, technical, and leadership teams.
Preferred Qualifications
- Experience with AWS cloud services and AI platforms.
- Knowledge of AWS Bedrock and enterprise AI solution architectures.
- Experience with: + FastAPI + AWS Lambda + Amazon S3 + Relational Databases + Vector Databases (PGVector)
- Understanding of semantic search, embeddings, retrieval architectures, and contextual AI systems.
- Experience with document management platforms and knowledge management systems.
- Familiarity with enterprise security, governance, and compliance frameworks.
- Experience leading global implementation projects and distributed technical teams.
- Prior Life Sciences, Healthcare, or regulated-industry experience is a plus.
Technical Expertise
Candidates should demonstrate experience in:
- Solution Architecture
- Generative AI
- AI Authoring Platforms
- Intelligent Document Processing (IDP)
- Prompt Engineering
- Retrieval-Augmented Generation (RAG)
- Enterprise Integrations
- API Design & Development
- AWS Cloud Services
- FastAPI
- AWS Lambda
- Amazon S3
- Vector Databases
- AI Governance
- Content Management
- Production Support & Operations
Salary and Other Compensation
The annual salary for this position is anticipated to be between $145,000 and $155,000 USD, depending on experience, qualifications, geographic location, 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 Insurance
- Health Savings Account (HSA) and Flexible Spending Accounts (FSA)
- Life Insurance and AD&D Coverage
- Short-Term and Long-Term Disability Insurance
- Paid Time Off (PTO) and Company Holidays
- 401(k) Plan with Company Contributions (subject to eligibility)
- Employee Stock Purchase Plan (where available)
- Employee Assistance Program (EAP)
- Parental Leave and Family Care Benefits
- Tuition Reimbursement and Professional Development Programs
- Access to Learning, Certification, and Career Development Opportunities
Benefits are subject to eligibility requirements and plan terms.
Why Cognizant
- Help shape the future of enterprise AI solutions.
- Work on innovative AI-powered document processing and authoring platforms.
- Partner with leading global enterprises on transformative digital initiatives.
- Gain exposure to cutting-edge Generative AI, cloud, and intelligent automation technologies.
- Access extensive career development, training, and certification opportunities.
- Join a global team committed to innovation, collaboration, and business transformation.
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, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability, protected veteran status, or any other characteristic protected by applicable law.
Applications will be accepted until the position is filled.
Salary Context
This $145K-$155K range is below the median 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 ($150K) sits 29% below the category median. Disclosed range: $145K to $155K.
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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