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About This Role
Job Title: Director, Product Engineering \- Practice Specialist (Data \& Ai)
City: San Francisco
State/Province: California
Posting Start Date: 7/2/26
Wipro Limited (NYSE: WIT, BSE: 507685, NSE: WIPRO) is a leading technology services and consulting company focused on building innovative solutions that address clients’ most complex digital transformation needs. Leveraging our holistic portfolio of capabilities in consulting, design, engineering, and operations, we help clients realize their boldest ambitions and build future\-ready, sustainable businesses. With over 230,000 employees and business partners across 65 countries, we deliver on the promise of helping our customers, colleagues, and communities thrive in an ever\-changing world. For additional information, visit us at www.wipro.com.
Role Overview
As a senior P\&L leader in strategic account growth, you will drive end to end revenue expansion across a portfolio of named enterprise accounts. This role requires deep technical fluency in Product Engineering, Cloud, and AI services to shape clients’ technology transformation roadmaps. You will cultivate C suite relationships, architect high impact solutions, and ensure exceptional delivery outcomes that position Wipro as a trusted innovation and engineering partner.
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Required Qualifications
- 10\+ years of enterprise technology sales with a minimum of 7\+ years in Product Engineering, Cloud, and AI/ML services, driving complex, multi\-tower engagements.
- Strong domain knowledge across SaaS platforms, AI/ML ecosystems, Hyperscalers (AWS/Azure/GCP)and Technology verticals.
- Proven ability to conceptualize and present technical proposals, including proactive solution narratives and responses to RFPs/RFIs.
- Demonstrated success in meeting or exceeding bookings, revenue, and margin quotas.
- Extensive experience driving AI and automation led transformations, including proactive modernization and consolidation programs.
- Working knowledge of GenAI, LLMOps, Agentic AI, and their application in the software development lifecycle (SDLC).
- Proven track record in building and accelerating pipeline within large, multi\-stakeholder enterprise accounts.
- Strong communication skills to simplify and articulate complex cloud architectures, AI solution patterns, and engineering roadmaps.
- Bachelor’s or Master’s degree in Engineering, Computer Science, IT, Business, or related fields.
- AI / Cloud certifications (AWS, GCP or Azure) strongly preferred.
- Prior experience in a technology product company, global systems integrator, or digital consulting firm.
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Core Responsibilities
- Lead strategic client conversations with technical depth—diagnosing business and engineering challenges and converting them into actionable AI, cloud, and product engineering roadmaps.
- Partner with Solution Architects to design end to end AI driven architectures, cloud modernization plans, and engineering transformation solutions backed by compelling ROI/value propositions.
- Own the complete Lead to Order lifecycle, from opportunity identification to solution shaping, deal strategy, proposal development, negotiation, and closure.
- Build and expand relationships across CxO, Engineering, Product, IT, and Operations teams, establishing Wipro as the preferred engineering and AI transformation partner.
- Proactively identify and shape large\-scale modernization, cloud, data, and AI transformation programs, aligning them with Wipro’s differentiated solutions and IP.
- Maintain a detailed Account Plan, including technology landscape analysis, competitor attack plan, stakeholder heatmaps, whitespace opportunities, and competitive positioning.
- Lead Steering Committees, QBRs, and delivery governance to ensure seamless execution, risk mitigation, accelerated Time to Value (TTV), and high customer satisfaction.
- Collaborate across Wipro’s global teams to bring industry use cases, GenAI accelerators, and engineering IP to clients.
- Develop a network of client champions and references who can attest to Wipro’s delivery quality, engineering expertise, and transformational impact.
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Why work at Wipro?
We pride ourselves on creating an inclusive workplace that provides equal opportunities to all persons regardless of their age, cultural background, sexual orientation, gender identity and expression, disability, veteran status, or anything else. If you only meet some of the requirements for this role, that's okay! We value a diverse range of backgrounds \& ideas and believe this is fundamental for our future success. So, if you have the curiosity to learn and the willingness to teach what you know, we'd love to hear from you.
Wipro has been globally recognized by several organizations for our commitment to sustainability, inclusion, and diversity. Social good is in our DNA, we believe in sustainability for the health of our planet, its inhabitants, and our business. For over 75 years we have operated as a purpose\-driven company with an unwavering commitment to our customers and our communities. Energized by what we call the Spirit of Wipro, we commit ourselves to being a catalyst for change – working to build a more just, equitable and sustainable society. Around 66% of Wipro’s economic ownership is pledged towards philanthropic purposes.
All our employees are expected to embody Wipro’s 5\-Habits for Success which are: Being Respectful, Being Responsive, Always Communicating, Demonstrate Stewardship, Building Trust.
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The expected compensation for this role ranges from $190,000\.00 to $265,000\.00\.
Final compensation will depend on various factors, including your geographical location, minimum wage obligations, skills, and relevant experience. Based on the position, the role is also eligible for Wipro’s standard benefits including a full range of medical and dental benefits options, disability insurance, paid time off (inclusive of sick leave), other paid and unpaid leave options.
Applicants are advised that employment in some roles may be conditioned on successful completion of a post\-offer drug screening, subject to applicable state law.
Wipro provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. Applications from veterans and people with disabilities are explicitly welcome.
Reinvent your world. We are building a modern Wipro. We are an end\-to\-end digital transformation partner with the boldest ambitions. To realize them, we need people inspired by reinvention. Of yourself, your career, and your skills. We want to see the constant evolution of our business and our industry. It has always been in our DNA \- as the world around us changes, so do we. Join a business powered by purpose and a place that empowers you to design your own reinvention.
Salary Context
This $190K-$265K 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 Wipro, 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. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($227K) sits 6% above the category median. Disclosed range: $190K to $265K.
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.
Wipro AI Hiring
Wipro has 12 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, MLOps Engineer, AI Architect. Positions span Dallas, TX, US, Richfield, MN, US, Austin, TX, US. Compensation range: $78K - $375K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% 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 4,317 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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