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About This Role
Work Model: Hybrid – US and are comfortable with up to 50% travel.
- Dallas / Chicago / Atlanta / NY\-NJ / San Francisco/ Los Angeles\-
Level: Senior Director
About the role:
This hybrid Senior Director\-level role is designed for a seasoned Commerce Solution Architect who can shape, govern, and sell end\-to\-end digital and agentic commerce architectures for complex enterprise clients. The role combines deep traditional commerce platform expertise with emerging agentic commerce capability, including Universal Commerce Protocol (UCP), Agent2Agent (A2A), Model Context Protocol (MCP), Agent Payments Protocol (AP2\), multi\-agent orchestration, and AI\-mediated purchase journeys. The Solutioner translates business vision into scalable commerce platforms, agent\-enabled experience models, and implementation\-ready architecture patterns that deliver measurable business value, preserve brand control, protect customer data, and improve transaction outcomes across global markets.
Key Responsibilities:
Commerce solution architecture and governance
- Drive discovery sessions with business, technology, and commerce stakeholders to elicit detailed requirements and translate them into clear solution objectives aligned to enterprise strategy.
- Design end\-to\-end digital commerce architectures that integrate storefronts, order management, payment services, loyalty, CPQ, ERP, PIM, DAM, CMS, CDP, analytics, and downstream enterprise systems.
- Create solution blueprints, reference models, integration patterns, and implementation guardrails for commerce platforms that enable consistent delivery across business units and client accounts.
- Define non\-functional requirements across performance, reliability, scalability, resilience, observability, security, privacy, and data protection to support peak commerce events and mission\-critical workloads.
- Review technical designs and delivery approaches to ensure alignment with approved commerce architecture, engineering standards, vendor constraints, and enterprise guardrails.
Agentic commerce and open protocol architecture
- Architect agentic commerce solutions that connect consumer AI surfaces, including Google AI Mode, Gemini, and third\-party agents, to merchant systems through UCP, A2A, MCP, API, and event\-driven integration patterns.
- Design multi\-agent orchestration patterns for discovery, inventory validation, loyalty application, cart creation, checkout, post\-purchase support, and human handoff across digital commerce journeys.
- Define implementation patterns for UCP primitives, Universal Cart concepts, identity linking, order management extensions, and emerging partner integrations as they apply to commerce transformation programs.
- Advise clients on brand sovereignty strategies that preserve UX control, first\-party data ownership, merchandising influence, margin integrity, and post\-purchase engagement when transactions occur inside AI\-mediated surfaces.
- Architect mitigation strategies for zero\-click commerce risks, including storefront traffic cannibalization, first\-party data leakage, brand experience displacement, and AI\-agent fulfillment handoff gaps.
Pre\-sales solutioning and pursuit leadership
- Serve as a senior solution architect for high\-priority commerce and agentic commerce pursuits, shaping technical narrative, solution strategy, delivery approach, and commercial feasibility.
- Develop differentiated RFP responses and proposal architectures that translate UCP, agentic AI, and commerce platform capabilities into quantified client business outcomes.
- Lead client discovery workshops with technology, digital, ecommerce, marketing, and commerce executives to diagnose current\-state gaps and define readiness for digital and agentic commerce modernization.
- Partner with delivery, pricing, staffing, and margin teams to scope complex commerce engagements and develop credible implementation roadmaps.
- Articulate the business value of proposed commerce solutions to client stakeholders and internal executives, connecting architecture decisions to growth, operational efficiency, customer experience, and risk reduction.
Thought leadership and market positioning
- Author and contribute to external and internal points of view, whitepapers, blogs, conference materials, and client\-facing presentations on agentic commerce architecture and implementation patterns.
- Represent the commerce architecture perspective at industry events, partner sessions, client briefings, and internal enablement forums.
- Build and maintain technical relationships with relevant commerce and AI ecosystem partners, including Google Commerce, Shopify, Salesforce, BigCommerce, payment providers, and cloud platform partners.
- Brief internal practice leaders, market teams, alliance leaders, and delivery teams on evolving agentic commerce protocols, platform capabilities, and competitive positioning.
- Convert lessons learned from pursuits and delivery into reusable solution archetypes, accelerators, playbooks, patterns, and standards.
Delivery enablement and capability development
- Mentor senior engineers, solution designers, and commerce architects on modern commerce architecture, agentic AI patterns, integration models, and delivery\-quality expectations.
- Build a broader technical community of practice for commerce architecture, UCP readiness, agentic AI enablement, and reusable engineering standards.
- Partner with consulting, AI, cloud, data, security, and delivery practices to create cross\-service commerce offerings and integrated solution models.
- Coordinate change planning so new commerce capabilities are introduced with minimal disruption, clear rollback strategies, and sustainment models.
- Drive continuous improvement by capturing lessons learned and updating reference patterns, solution assets, technical standards, and reusable components
Required Skills and Qualifications:
- Requires extensive experience designing and delivering large\-scale digital commerce architectures using modern platforms, plus deep understanding of commerce domain processes and integration patterns.
- Requires significant practical experience in commerce technical implementation, including catalog modeling, order lifecycle flows, payment orchestration, tax and pricing rules, customer account management, loyalty, and checkout experiences.
- Requires proven track record in digital commerce solution architecture, including multi\-team engagements and architectural governance from concept through deployment.
- Requires strong ability to operate at both blueprint level with executive stakeholders and detailed technical level with engineering and delivery teams.
- Nice to have experience with cloud\-based commerce platforms, microservice\-oriented architectures, composable commerce, MACH patterns, and event\-driven integration strategies.
- Nice to have exposure to analytics, personalization, recommendation engines, targeted promotions, experimentation frameworks, and data strategies that convert commerce interactions and behavioral signals into insight.
- Nice to have experience working in hybrid delivery models with distributed teams while maintaining strong alignment among business vision, architecture decisions, implementation practices, and delivery outcomes.
Certifications Required \- Experience:
- 12–18 years of professional experience, with substantial experience in senior solution architecture, digital commerce, technical leadership, consulting, systems integration, or commerce platform environments.
- At least 8 years in commerce architecture, solutioning, technical leadership, or related roles supporting enterprise retail, consumer goods, manufacturing, travel, B2B, B2C, or D2C commerce environments.
- Demonstrable record shaping, architecting, or winning complex commerce transformation programs for enterprise clients.
- Direct or adjacent experience with AI agent frameworks, agentic system design, or AI\-enabled commerce use cases is strongly preferred.
- Preferred certifications or partner credentials may include Google Cloud, Google Commerce, Gemini, Shopify Plus Partner, Salesforce, Adobe, SAP Commerce, or comparable commerce/cloud credentials.
Cognizant will only consider applicants for this position who are legally authorized to work in the United States without requiring company sponsorship now or at any time in the future.
The annual salary for this position is between $220,000 \- $240,000 depending on the experience and other qualifications of the successful candidate. 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 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:
Compensation 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.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview.
While our system allows application in all languages, job required language(s) and proficiency level(s) vary. However, basic English proficiency is required for Company\-wide communications purposes
The Cognizant community:
We are a high caliber team who appreciate and support one another. Our people uphold an energetic, collaborative and inclusive workplace where everyone can thrive.
Cognizant is a global community with more than 300,000 associates around the world.
We don’t just dream of a better way – we make it happen.
We look after our people, clients, company, communities and climate by doing what’s right.
We cultivate an innovative environment where you can build the career path that’s right for you.
\#LI\-RP1
Salary Context
This $220K-$240K range is above the 75th percentile 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 ($230K) sits 7% above the category median. Disclosed range: $220K to $240K.
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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