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About This Role
AI Customer Engineer III
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:
- Lead structured client discovery and art\-of\-the\-possible ideation sessions identifying high\-value AI use cases and translating ambiguous needs into clear solution hypotheses.
- Embed with clients and rapidly prototype GenAI\-enabled solutions building working software that demonstrates business impact with speed and precision.
- Define end\-to\-end AI solution architectures including agentic platforms, data/ML pipelines, integration patterns, human\-in\-the\-loop controls, and partner components.
- Apply architecture decisions that balance quality, safety, latency, cost, and model risk ensuring solutions are enterprise\-grade and production\-ready.
- Build and sustain trusted relationships with client senior leadership operating with executive presence to instill confidence, manage expectations, and position Cognizant as a long\-term AI partner.
- Partner with clients throughout the MVP build cycle managing expectations, communicating progress clearly to executive and technical audiences, enabling end users, and ensuring a clean, well\-documented handoff to delivery and service line teams for scaled implementation.
- Develop client\-ready deliverables including proposals, Statements of Work, architecture decks, and value frameworks tailored for executive audiences.
- Orchestrate pursuit teams across sales, industry specialists, engineering, and delivery to progress complex AI opportunities through deal closure.
- Coach and develop junior engineers through regular feedback, mentoring, and stretch assignments.
- Mentor and develop junior engineers through deal reviews, coaching, and development planning.
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
- Experience: 5\+ years in AI/ML engineering, solution architecture, pre\-sales, or technical consulting.
- Executive Presence: Demonstrated ability to engage confidently with senior client stakeholders, lead executive\-level discussions, and represent technical solutions with authority and clarity.
- Pre\-Sales \& Solutioning: Demonstrated ability to shape AI opportunities, write proposals, and estimate complex solutions.
- Client Relationship Management: Proven ability to build and sustain trusted relationships with senior client stakeholders throughout the engagement lifecycle.
- Nice to Have: Experience building MVPs during sales cycles; background in AI consulting, technical advisory, or a specific enterprise vertical.
These will help you stand out
- Executive Presence \& Influence – Engaging with senior client leadership and internal executives with authority, clarity, and confidence; inspiring trust at every level of the client organization.
- Ideation \& Art\-of\-the\-Possible – Stretching client thinking to uncover new AI value creation opportunities.
- Solutioning Excellence – Designing scalable, feasible, value\-led solutions for diverse client environments.
- AI/ML Technical Depth – Strong mastery of GenAI, LLMs, ML engineering, agentic architectures, and production AI deployment.
- Handoff \& Continuity – Ensuring MVP\-to\-delivery transitions are thorough, well\-documented, and set service line teams up for successful scaled implementation.
- High Agency – Ability to navigate ambiguity, operate autonomously, and represent Cognizant at the highest level in client environments.
Technical Skills \& Tools
- Languages \& Engineering: Strong production coding across Python and at least one additional language; fluency in enterprise integration patterns.
- AI/ML \& GenAI: Deep production expertise in LLMs, agentic architectures, evaluation frameworks, and MLOps/LLMOps at scale.
- Cloud Platforms: Multi\-hyperscaler depth across AWS Bedrock, Google Vertex AI, and Azure AI Foundry; owns model selection, routing, open\-weight self\-hosting, and cost/latency optimization.
- Agent \& Orchestration Frameworks: Designs multi\-agent and stateful agent systems; selects among LangGraph, CrewAI, Microsoft Agent Framework, and vendor SDKs based on control, latency, and cost trade\-offs; applies orchestration patterns (supervisor/worker, human\-in\-the\-loop checkpoints).
- RAG \& Retrieval: Designs enterprise retrieval architectures; evaluates vector store and indexing trade\-offs for accuracy, scale, and cost.
- LLMOps — Eval \& Observability: Establishes the evaluation and observability strategy for an engagement (LangSmith, Langfuse, Arize Phoenix, or Braintrust), including self\-hosted options for data\-residency requirements.
- Responsible AI \& Governance: Embeds Responsible AI by design — model risk, safety, and bias controls — aligned to NIST AI RMF, ISO 42001, and the EU AI Act; applies sector compliance (SOC 2, HIPAA, PCI) as relevant.
- Practice Contribution: Codifies reusable patterns, accelerators, and reference implementations that build team IP.
Salary and Other Compensation:
The annual salary for this position is between $100,000 \-$180,000 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 $100K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($140K) sits 23% below the category median. Disclosed range: $100K to $180K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Cognizant AI Hiring
Cognizant has 18 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer, AI Architect, Research Scientist. Positions span Santa Clara, CA, US, Warren, MI, US, Atlanta, GA, US. Compensation range: $84K - $280K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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