- Location
- Bengaluru Millenia, India
- Type
- Full-time
- Department
- IT
- Seniority
- Manager
- Education
- Master
- Clearance
- Required
- Closing date
- Today
- Source
- Workday
Description
Line of Service
AdvisoryIndustry/Sector
Not ApplicableSpecialism
Data, Analytics & AIManagement Level
ManagerJob Description & Summary
At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth.Those in artificial intelligence and machine learning at PwC will focus on developing and implementing advanced AI and ML solutions to drive innovation and enhance business processes. Your work will involve designing and optimising algorithms, models, and systems to enable intelligent decision-making and automation.
Job Description & Summary:
We're looking for a Senior AI/ML Engineer who can design, build, and deploy scalable ML, GenAI, and Agentic AI systems across cloud environments (GCP preferred) with strong focus on productionization, automation, and business impact. You'll work across demand forecasting, RAG-based intelligent applications, autonomous multi-agent systems, and enterprise AI integration.
Responsibilities:
Build end-to-end ML/AI pipelines (data → model → deployment → monitoring)
Develop and deploy ML, Deep Learning, NLP, and GenAI models in production
Design and implement RAG systems — retrieval, chunking, embeddings, vector search, and prompt engineering
Build Agentic AI solutions — autonomous agents, multi-agent workflows, tool-calling, planning, and memory
Build and optimize time series forecasting models (demand forecasting, inventory planning)
Implement MLOps pipelines — CI/CD, model monitoring, drift detection, governance
Optimize models for performance, cost, and latency
Integrate AI systems with enterprise APIs, data platforms, and customer-facing applications
Design scalable LLM inference architectures for efficient deployment
Collaborate with data scientists, product managers, engineers, and business stakeholders in Agile teams
Debug, optimize, and enhance ML models for quality and performance improvements
Mentor team members and present technical findings to diverse audiences
Stay current with AI/GenAI trends and evaluate emerging tools and frameworks
Mandatory skill sets:
Build end-to-end ML/AI pipelines (data → model → deployment → monitoring)
Develop and deploy ML, Deep Learning, NLP, and GenAI models in production
Design and implement RAG systems — retrieval, chunking, embeddings, vector search, and prompt engineering
Build Agentic AI solutions — autonomous agents, multi-agent workflows, tool-calling, planning, and memory
Build and optimize time series forecasting models (demand forecasting, inventory planning)
Implement MLOps pipelines — CI/CD, model monitoring, drift detection, governance
Optimize models for performance, cost, and latency
Integrate AI systems with enterprise APIs, data platforms, and customer-facing applications
Design scalable LLM inference architectures for efficient deployment
Collaborate with data scientists, product managers, engineers, and business stakeholders in Agile teams
Debug, optimize, and enhance ML models for quality and performance improvements
Mentor team members and present technical findings to diverse audiences
Stay current with AI/GenAI trends and evaluate emerging tools and frameworks
Preferred skill sets:
Build end-to-end ML/AI pipelines (data → model → deployment → monitoring)
Develop and deploy ML, Deep Learning, NLP, and GenAI models in production
Design and implement RAG systems — retrieval, chunking, embeddings, vector search, and prompt engineering
Build Agentic AI solutions — autonomous agents, multi-agent workflows, tool-calling, planning, and memory
Build and optimize time series forecasting models (demand forecasting, inventory planning)
Implement MLOps pipelines — CI/CD, model monitoring, drift detection, governance
Optimize models for performance, cost, and latency
Integrate AI systems with enterprise APIs, data platforms, and customer-facing applications
Design scalable LLM inference architectures for efficient deployment
Collaborate with data scientists, product managers, engineers, and business stakeholders in Agile teams
Debug, optimize, and enhance ML models for quality and performance improvements
Mentor team members and present technical findings to diverse audiences
Stay current with AI/GenAI trends and evaluate emerging tools and frameworks
Years of experience required:
8-12 years
Education qualification:
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (60% above)
Education (if blank, degree and/or field of study not specified)
Degrees/Field of Study required: Master of Engineering, Bachelor of EngineeringDegrees/Field of Study preferred:Certifications (if blank, certifications not specified)
Required Skills
Generative AIOptional Skills
Accepting Feedback, Accepting Feedback, Active Listening, AI Implementation, Analytical Thinking, C++ Programming Language, Coaching and Feedback, Communication, Complex Data Analysis, Creativity, Data Analysis, Data Infrastructure, Data Integration, Data Modeling, Data Pipeline, Data Quality, Deep Learning, Embracing Change, Emotional Regulation, Empathy, GPU Programming, Inclusion, Intellectual Curiosity, Java (Programming Language), Learning Agility {+ 30 more}Desired Languages (If blank, desired languages not specified)
Travel Requirements
Available for Work Visa Sponsorship?
Government Clearance Required?
Job Posting End Date
May 17, 2026