IN_Manager_AI/ML Engineer_GCC_Advisory_Bangalore

Line of Service

Industry/Sector

Specialism

Management Level

Job Description & Summary

*Why PWC

At PwC, you will be part of a vibrant community of solvers that leads with trust and creates distinctive outcomes for our clients and communities. This purpose-led and values-driven work, powered by technology in an environment that drives innovation, will enable you to make a tangible impact in the real world. We reward your contributions, support your wellbeing, and offer inclusive benefits, flexibility programmes and mentorship that will help you thrive in work and life. Together, we grow, learn, care, collaborate, and create a future of infinite experiences for each other. Learn more 

about us

.

At PwC, we believe in providing equal employment opportunities, without any discrimination on the grounds of gender, ethnic background, age, disability, marital status, sexual orientation, pregnancy, gender identity or expression, religion or other beliefs, perceived differences and status protected by law. We strive to create an environment where each one of our people can bring their true selves and contribute to their personal growth and the firm’s growth. To enable this, we have zero tolerance for any discrimination and harassment based on the above considerations. "

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 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: 

7-12 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)

Certifications (if blank, certifications not specified)

Required Skills

Optional Skills

Desired Languages (If blank, desired languages not specified)

Travel Requirements

Available for Work Visa Sponsorship?

Government Clearance Required?

Job Posting End Date