IN_Senior Associate_ML Ops Lead_GCC_Advisory_Bangalore

PwC; Bengaluru; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

PwC is seeking an ML Ops Lead with 8+ years of experience to own the end-to-end ML lifecycle, from experimentation to production deployment and ongoing monitoring.

You will design cloud-native MLOps pipelines on Azure, manage ML infrastructure (AKS), and implement CI/CD, observability, and reproducibility across ML workflows. Collaboration with data scientists, engineers, and stakeholders is essential for success.

Qualifications

• 8+ years of experience in Machine Learning Engineering, MLOps, Data Science, or related fields.
• Strong background in ML with software engineering skills for production deployments.
• Experience with large-scale datasets and scalable ML pipelines.
• Expertise with modern ML methods, including Transformers for NLP.

Responsibilities

• Lead the full ML system lifecycle from experimentation to production deployment and 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 AKS.
• Implement CI/CD, observability, and reproducibility in ML workflows to productionise models.
• Productionize ML models as robust APIs and batch systems in healthcare workflows.
• Collaborate with data scientists, engineers, and stakeholders to deliver scalable ML systems.
• Monitor model performance, detect drift, and drive continuous improvement in production.
• Mentor teams on MLOps practices and cloud engineering.

Skills

Python

Machine learning

Deep learning

Education

Bachelor of Engineering

Tools

Azure

AKS

Job description

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 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

Position Overview: ML Ops Lead

Responsibilities

• 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

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

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 Softw