Interested in this AI/ML Engineer role at Cognizant?
Apply Now →Skills & Technologies
About This Role
About the role
As an AI Customer Engineer III in Cognizant's AI Market Unit, you will lead complex AI solution shaping and MVP delivery efforts while playing a central role in advancing strategic client opportunities. With deep technical expertise and strong executive presence, you will run ideation workshops, define end\-to\-end AI architectures, and own the development of compelling proposals that connect technical excellence to measurable business outcomes. Embedded in client environments, you will build trusted relationships with senior stakeholders, manage expectations across the engagement lifecycle, and ensure a well\-orchestrated handoff to delivery and service line teams. You will also develop the engineers around you, building capability and quality across the AI Market Unit.
In this role, you will:
Own the technical success of one or more strategic customer engagements, serving as primary technical authority across architecture, delivery quality, and client executive relationships.
Define the client's agentic AI roadmap: identify high\-value use cases, sequence the build, set success metrics, and tie outcomes to business impact — cost, revenue, risk, and speed.
Architect enterprise\-scale multi\-agent systems including agent orchestration topology, inter\-agent communication protocols, memory and state management at scale, fail\-safe and escalation paths, and governance / audit layers.
Drive build\-vs\-buy decisions, model selection, and platform choices with clear\-eyed view of client constraints — security, compliance, data sovereignty, and existing tech stack.
Establish evaluation and observability standards for the engagement: evals infrastructure, drift detection, model performance monitoring, agent behavior auditing, and rollback strategies.
Represent Cognizant at CTO / CAIO\-level discussions, translating complex AI system behavior into board\-ready risk and value narratives.
Partner with clients throughout MVP build and handoff, managing C\-suite expectations, communicating progress with authority and clarity, and ensuring a comprehensive transition to delivery and service line teams.
Identify repeatable deployment patterns across engagements and productize them as accelerators, reference architectures, and reusable agents within the AI Market Unit.
Recruit, mentor, and develop FDE team members; raise the capability bar of the entire practice through internal tech talks, architecture reviews, and hands\-on coaching.
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 2\-3 days a week in a client or Cognizant office. Regardless of your working arrangement, we are here to support a healthy work\-life balance though our various wellbeing programs.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
Overall experience: 10\+ years.
Technical Depth: Deep, still\-current hands\-on engineering — you architect and you build; you have not drifted into pure oversight. We will ask you to show recent work and go three levels deep on it.
Agentic Frameworks: Deep expertise in at least two agentic frameworks; has extended or contributed to open\-source AI tooling.
Cloud Fluency: Multi\-cloud depth across AWS Bedrock, Azure AI Foundry, and GCP Vertex AI; knows where each excels and fails.
Delivery Ownership: Proven ability to own a full engagement or product from ambiguity to a deployed, value\-creating outcome.
Executive Presence: Can sit across from a CxO, be challenged, and hold the room on technical and business terms — credibility earned, not assumed.
Judgment Under Ambiguity: Sound instincts on what to build, what to kill, and when to push back on a customer.
.
These will help you stand out
Track record of multiple successful GenAI / agentic AI production deployments at enterprise scale.
Has built or scaled an engineering team — hiring, mentoring, technical career development.
Has shaped product strategy — turned field learnings into platform features or accelerator IP.
Demonstrated ability to navigate organizational resistance and drive AI adoption across large enterprises.
Domain depth in financial services, healthcare, supply chain, retail, or manufacturing.
Technical Skills \& Tools
Agentic Frameworks: Deep expertise in at least two frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalent); has extended or contributed to open\-source AI tooling; designs enterprise\-scale multi\-agent orchestration topology.
Cloud Platforms: Multi\-cloud fluency across AWS Bedrock, Azure AI Foundry, and GCP Vertex AI; knows where each excels and fails; drives platform selection and model routing decisions.
Advanced AI Engineering: Fine\-tuning, RLHF, and model adaptation strategies for enterprise domains; distributed systems fundamentals for agent systems under load, partial failure, and adversarial inputs.
AI Security \& Red\-Teaming: Prompt injection, jailbreaks, data leakage vectors, and mitigations; enterprise IAM, data residency, and compliance (SOC 2, HIPAA, PCI).
LLMOps — Eval \& Observability: Establishes evals infrastructure, drift detection, model performance monitoring, agent behavior auditing, and rollback strategies for the engagement; owns the observability standard.
Responsible AI \& Governance: Sets Responsible AI standards aligned to NIST AI RMF, ISO 42001, EU AI Act, and sector\-specific compliance; embeds governance and audit layers into architecture.
Emerging\-Tech Radar: Actively evaluates new frameworks, models, and platforms; guides the team on when to adopt, trial, or hold.
Salary and Other Compensation:
The annual salary for this position is between $117,000 \-$210,000 depending on experience and other qualifications of the successful candidate. This position is eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans.
Benefits
This position is also eligible for Cognizant’s discretionary annual incentive program and stock awards, based on performance and subject to the terms of Cognizant’s applicable plans.
Benefits: Cognizant offers the following benefits for this position, subject to apply
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, based on applicable law.
Work Authorization
Must be legally authorized to work in the United States without the need for employer sponsorship, now or any time in the future.
Salary Context
This $117K-$210K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($163K) sits 12% below the category median. Disclosed range: $117K to $210K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Cognizant AI Hiring
Cognizant has 16 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Research Engineer, Research Scientist. Positions span Seattle, WA, US, Plano, TX, US, Rockville, MD, US. Compensation range: $84K - $218K.
Location Context
AI roles in Seattle pay a median of $227,400 across 1,128 tracked positions. That's 13% above the national 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.