Level

PwCPosted 1w ago

IN_Senior Associate_ML Ops Lead_GCC_Advisory_Bangalore

IN_Senior Associate_ML Ops Lead_GCC_Advisory_Bangalore at PwC scores 95 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

Bengaluru MilleniaseniorFull time

AI in this role

ml-opsnlp

Line of Service

Advisory

Industry/Sector

Not Applicable

Specialism

Data, Analytics & AI

Management Level

Senior Associate

Job Description & Summary

At PwC, our people in data and analytics focus on leveraging data to drive insights and make informed business decisions. They utilise advanced analytics techniques to help clients optimise their operations and achieve their strategic goals.

In data analysis at PwC, you will focus on utilising advanced analytical techniques to extract insights from large datasets and drive data-driven decision-making. You will leverage skills in data manipulation, visualisation, and statistical modelling to support clients in solving complex business problems.

*Why PWCAt 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 

A career within Data & Analytics Services will provide you with the opportunity to help our clients leverage transformation to enhance their customer experiences. 

 

*Responsibilities:  

Position Overview: ML Ops Lead 

  • Lead the full ML system lifecycle: from experimentation and model development to production deployment and ongoing monitoring. 

  • Design and build scalable, cloud-native MLOps pipelines for training, validation, deployment, and lifecycle management. 

  • Develop and manage ML infrastructure on Azure, with emphasis on Azure Kubernetes Service (AKS). 

  • Implement best practices for model versioning, CI/CD, observability, and reproducibility in ML workflows. 

  • Productionize ML models as robust, low-latency APIs and batch systems integrated into healthcare workflows. 

  • Collaborate cross-functionally with data scientists, engineers, and stakeholders to deliver scalable ML systems. 

  • Monitor model performance, detect drift, and ensure continuous model improvement in production. 

  • Mentor teams on MLOps practices, cloud engineering, and designing production-grade ML systems. 

  • Stay up-to-date with evolving trends in MLOps, distributed systems, and the Azure AI ecosystem. 

Who We Are Looking For: 

  • 8+ years of experience in Machine Learning Engineering, MLOps, Data Science, or related quantitative fields. 

  • Strong background in machine learning complemented by software engineering skills and systems thinking for production deployments. 

  • Proven experience with large-scale, high-dimensional datasets and building scalable, reliable ML pipelines. 

  • Expertise with modern ML methods, including Transformers for NLP and representation learning. 

  • Demonstrated success taking models from experimentation through to production using MLOps best practices (CI/CD, automation, orchestration). 

  • Ability to independently own ambiguous problems and deliver end-to-end ML solutions. 

  • Excellent collaboration, communication, and stakeholder management skills. 

  • Hands-on experience deploying, monitoring, and managing ML models in cloud environments, preferably Azure and AKS. 

  • Healthcare domain experience, particularly with healthcare revenue cycle, is a plus. 

Mandatory skill sets:   

Python, Machine learning , Deep learning  

Preferred skill sets:   

Python, Machine learning , Deep learning  

Years of experience required:   

8+ years 

Employment Type : Full Time, Permanent 

Education qualification:   

Full time B. E    

Education (if blank, degree and/or field of study not specified)

Degrees/Field of Study required: Bachelor of Engineering, Master of Engineering

Degrees/Field of Study preferred:

Certifications (if blank, certifications not specified)

Required Skills

ML Platforms, Operations Security

Optional Skills

Accepting Feedback, Accepting Feedback, Active Listening, Algorithm Development, Alteryx (Automation Platform), Analytical Thinking, Analytic Research, Big Data, Business Data Analytics, Communication, Complex Data Analysis, Conducting Research, Creativity, Customer Analysis, Customer Needs Analysis, Dashboard Creation, Data Analysis, Data Analysis Software, Data Collection, Data-Driven Insights, Data Integration, Data Integrity, Data Mining, Data Modeling, Data Pipeline {+ 38 more}

Desired Languages (If blank, desired languages not specified)

Travel Requirements

Not Specified

Available for Work Visa Sponsorship?

No

Government Clearance Required?

No

Job Posting End Date

August 5, 2026

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Ml OpsNlp

Questions you could be asked

  1. How do you monitor a model once it's live, and how do you know it needs retraining?
  2. What NLP problem have you worked on, and how did you measure whether it actually worked?
  3. How would you decide a model or AI system is ready to ship?
  4. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: Ml Ops and Nlp. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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