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About This Role
Job Title: GCP Gen AI Architect (CONTRACT) City: New York City State/Province: New York 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 this role is to develop minimum viable product (MVP) and comprehensive AI solutions that meet and exceed client s expectations and add value to business.
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Do
- Manage the product/ solution development using the desired AI techniques + Lead development and implementation of custom solutions through thoughtful use of modern AI technology + Review and evaluate the use cases and decide whether a product can be developed to add business value + Create the overall product development strategy and integrating with the larger interfaces + Create AI models and framework and implement them to cater to a business problem + Draft the desired user Interface and create AI models as per business problem + Analyze technology environment and client requirements to define product solutions using AI framework/ architecture + Implement the necessary security features as per product s requirements + Review the used case and see the latest AI that can be used in product s development + Identify problem areas and perform root cause analysis and provide relevant solutions to the problem + Tracks industry and application trends and relates these to planning current and future AI needs + Create and delegate work plans to the programming team for product development + Interact with Holmes advisory board for knowledge sharing and best practices
- Responsible for developing and maintaining client relationships with the key strategic partners and decision makers + Drive discussions and provide consultation around product design as per customer needs + Participate in client interactions and gather insights regarding product development + Interact with vertical delivery and business teams and provide and correct responses to RFP/ client requirements + Assist in product s demonstration and receive feedback from the client + Design presentations for seminars, meetings and enclave primarily focused over product
- Team Management + Resourcing
- Forecast talent requirements as per the current and future business needs
- Hire adequate and right resources for the team + Talent Management
- Ensure adequate onboarding and training for the team members to enhance capability & effectiveness
- Build an internal talent pool and ensure their career progression within the organization
- Manage team attrition
- Drive diversity in leadership positions + Performance Management
- Set goals for the team, conduct timely performance reviews and provide constructive feedback to own direct reports
- Ensure that the Performance Nxt is followed for the entire team + Employee Satisfaction and Engagement
- Lead and drive engagement initiatives for the team
- Track team satisfaction scores and identify initiatives to build engagement within the team
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Deliver
No. Performance Parameter Measure
- Continuous technical project management & delivery Adoption of new technologies, IP creation, MVP creation, Number of patents filed, Research papers created
- Client Centricity No. of automation done, On-Time Delivery, cost of delivery, optimal resource allocation
- Capability Building & Team Management % trained on new age skills, Team attrition %, Number of webinars conducted (internal/external)
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Role - GCP Gen AI Architect
Location: Irving/ Jacksonville
Experience: 15+ Years
Domain: Banking, Fraud, Risk & Controls
Position Summary
We are seeking a highly experienced Generative AI Architect to lead the architecture, design, and implementation of next-generation AI solutions. The ideal candidate should possess deep expertise in Generative AI, Agentic AI, GCP Cloud Architecture, Customer Data Platforms (CDP), and enterprise-scale banking solutions, with a proven track record of delivering AI-driven transformation programs.
The role requires close collaboration with business stakeholders, product teams, data architects, risk teams, and engineering leadership to build scalable, secure, and compliant AI solutions leveraging Google Cloud's AI ecosystem.
Key Responsibilities
Define and drive the overall AI and data architecture strategy for the OMAI program.
Architect enterprise-scale GenAI solutions leveraging LLMs, Agentic AI, RAG, Knowledge Graphs, and AI Agents.
Design and implement Customer Data Platform (CDP) integrations to enable unified customer intelligence and personalized AI experiences.
Build scalable AI platforms using Google Cloud AI and ML services.
Lead architecture reviews, solution design workshops, and technical governance.
Design secure AI architectures compliant with banking, risk, regulatory, and data privacy requirements.
Partner with Product Owners, Data Engineering, Risk, Fraud, and Compliance teams to deliver business outcomes.
Establish AI MLOps, LLMOps, observability, monitoring, and responsible AI frameworks.
Drive AI adoption through reusable architecture patterns, frameworks, and best practices.
Provide technical leadership and mentoring to architects and engineering teams.
Required Skills
Generative AI & Agentic AI
LLMs including GPT, Gemini, Claude, Llama, and open-source models.
RAG architecture, vector databases, embeddings, semantic search.
Multi-agent and Agentic AI frameworks.
Prompt Engineering and Prompt Optimization.
Fine-tuning approaches such as LoRA and QLoRA.
AI Guardrails, Responsible AI, Explainability, and Hallucination Reduction.
LangChain, LangGraph, LlamaIndex, Semantic Kernel.
GCP Architecture
Vertex AI
Gemini Models
Document AI
BigQuery
Dataflow
Pub/Sub
Cloud Run
GKE
Cloud Functions
Cloud Storage
Dataproc
IAM and Security Architecture
Customer Data Platform (CDP)
Enterprise Customer Data Platform architecture.
Customer 360° data modeling.
Identity Resolution and Master Data Management.
Real-time customer insights and personalization.
Data governance, consent management, and privacy controls.
Integration with marketing, analytics, and AI ecosystems.
Data & AI Engineering
Python
SQL
Distributed Data Processing
Feature Engineering
Vector Databases (Pinecone, Vertex AI Vector Search, Weaviate, FAISS)
Knowledge Graphs
Data Lake and Data Mesh concepts
Banking Domain Expertise
Banking Operations
Fraud Management
Risk & Controls
Regulatory Compliance
Customer Servicing
Operational Excellence Programs
Financial Crimes/Fraud Analytics
Mandatory Skills: Generative AI .
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 Architect roles in our dataset (median: $175K across 37 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 9,079 AI roles we're tracking, AI Architect positions make up 1% of the market. At Wipro, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $224,070 based on 236 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($97K) sits 56% below the category median. Disclosed range: $60K to $135K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Wipro AI Hiring
Wipro has 13 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Architect. Positions span Bellevue, WA, US, Bengaluru, Karnataka, India, Texas City, TX, US. Compensation range: $83K - $180K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% above the national median.
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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