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About This Role
Job Title: Senior AI Customer Data Platform Engineer City: Irving State/Province: Texas Posting Start Date: 10/5/26 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 Purpose
The purpose of the role is to create and engineer exceptional product architectural design for existing and new products and enable delivery teams to provide exceptional client engagement and satisfaction.
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Do
- Define product requirements and design needs by displaying complete understanding of product vision and business requirements + Develop architectural designs for the new and existing products
- Part Implementation of derived solution
- Debug and Solve critical problems during implementation
- Evangelize Architecture to the Project and Customer teams to achieve the final solution.
- Constant analysis and monitoring of the product solution
- Continuously improve and simplify the design, optimize cost and performance
- Understand market- driven business needs and objectives; technology trends and requirements to define architecture requirements and strategy
- Create a product-wide architectural design that ensures systems are scalable, reliable, and compatible with different deployment options
- Develop theme-based Proof of Concepts (POCs) in order to demonstrate the feasibility of the product idea and realise it as a viable one
- Analyse, propose and implement the core technology strategy for product development
- Conduct impact analyses of changes and new requirements on the product development effort
- Provide solutioning of RFPs received from clients and ensure overall product design assurance as per business needs + Collaborate with sales, development, consulting teams to reconcile solutions to architecture + Analyse technology environment, enterprise specifics, client requirements to set a product solution design framework/ architecture + Provide technical leadership to the design, development and implementation of custom solutions through thoughtful use of modern technology + Define and understand current state product features and identify improvements, options & tradeoffs to define target state solutions + Clearly articulate, document and sell architectural targets, recommendations and reusable patterns and accordingly propose investment roadmaps + Validate the solution/ prototype from technology, cost structure and customer differentiation point of view + Identify problem areas and perform root cause analysis of architectural design and solutions and provide relevant solutions to the problem + Tracks industry and application trends and relates these to planning current and future IT needs
- Provides technical and strategic input during the product deployment and deployment + Support Delivery team during the product deployment process and resolve complex issues + Collaborate with delivery team to develop a product validation and performance testing plan as per the business requirements and specifications. + Identifies implementation risks and potential impacts.
- Maintain product roadmap and provide timely inputs for product upgrades as per the market needs
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- Competency Building and Branding + Ensure completion of necessary trainings and certifications + Develop Proof of Concepts (POCs), case studies, demos etc. for new growth areas based on market and customer research + Develop and present a point of view of Wipro on product design and architect by writing white papers, blogs etc. + Attain market referencability and recognition through highest analyst rankings, client testimonials and partner credits + Be the voice of Wipro s Thought Leadership by speaking in forums (internal and external) + Mentor developers, designers and Junior architects for their further career development and enhancement + Contribute to the architecture practice by conducting selection interviews etc
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Deliver
No. Performance Parameter Measure
- Product design, engineering and implementation CSAT, quality of design/ architecture, FTR, delivery as per cost, quality and timeline, POC review and standards
- Capability development % trainings and certifications completed, mentor technical teams, Thought leadership content developed (white papers, Wipro PoVs)
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Location: Irving, TX / Jacksonville, FL/ Tampa, FL / New York City, NY
Experience: 8+ Years
Domain: Banking & Financial Services (Preferred)
Job Summary
We are seeking a Senior AI Customer Data Platform (CDP) Engineer with strong expertise in Customer Data Platforms, Generative AI, and Data-Driven Personalization. The ideal candidate will design and build AI-powered customer engagement solutions leveraging customer 360 data, LLMs, RAG, AI Agents, and enterprise data platforms to support banking and financial services use cases.
Key Responsibilities
Design and implement AI-powered solutions using Customer Data Platforms (CDP) and Generative AI.
Build customer intelligence, personalization, and customer 360 solutions.
Develop AI chatbots, virtual assistants, and customer service automation platforms.
Integrate LLMs with customer data, CRM systems, marketing platforms, and enterprise applications.
Build RAG applications leveraging customer knowledge repositories and structured/unstructured data.
Develop APIs and AI services using Python.
Implement data ingestion, identity resolution, customer segmentation, and activation use cases.
Optimize prompts, model performance, and AI response quality.
Collaborate with business, product, data, and architecture teams to deliver AI-driven outcomes.
Ensure compliance with data privacy, governance, and Responsible AI standards.
Core Skills (Must Have)
AI Customer Data Platform
Customer Data Platforms (CDP)
Customer 360 Architecture
Customer Identity Resolution
Customer Segmentation
Data Activation & Personalization
CRM Integration (Salesforce preferred)
Generative AI
Azure OpenAI / OpenAI APIs
LLMs (GPT, Claude, Gemini, Llama)
Prompt Engineering
RAG Architecture
LangChain / LlamaIndex
AI Agents / Agentic AI
Data & Integration
Customer Data Modeling
Data Pipelines & ETL
SQL
REST APIs
Data Governance
Development
Python (Mandatory)
FastAPI / Flask
Microservices
Cloud & DevOps
Azure or GCP
Git / GitHub
Docker
CI/CD
Preferred Skills
Banking & Financial Services domain experience.
Experience with customer onboarding, servicing, fraud, marketing, or personalization platforms.
Knowledge of MLOps and AI Governance.
Experience integrating AI with Salesforce Data Cloud, Adobe Experience Platform, Tealium, Segment, or similar CDP platforms.
Exposure to GitHub Copilot and AI-assisted development.
Nice-to-Have
LangGraph
Semantic Kernel
Hugging Face
Azure AI Foundry
Pinecone / Chroma / FAISS
Knowledge Graphs
MCP (Model Context Protocol)
Experience Expectations
8+ years of software engineering, data engineering, or AI solution development experience.
2+ years of hands-on experience with Generative AI and LLM applications.
Experience implementing Customer 360 or CDP solutions.
Hands-on experience integrating customer data with AI/ML models.
Experience building at least one production-grade AI or RAG application.
Strong understanding of customer data privacy, security, and regulatory requirements.
Mandatory Skills: Customer Data Platform .
Experience: 5-8 Years .
The expected compensation for this role ranges from $60,000 to $135,000 .
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 $60K-$135K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($97K) sits 54% below the category median. Disclosed range: $60K to $135K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Wipro AI Hiring
Wipro has 7 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Positions span New York, NY, Irving, TX, US, Southlake, TX, US. Compensation range: $135K - $231K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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