Job Description

Line of Service

Advisory

Industry/Sector

Not Applicable

Specialism

Data, Analytics & AI

Management Level

Associate

& 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.

In data engineering at PwC, you will focus on designing and building data infrastructure and systems to enable efficient data processing and analysis. You will be responsible for developing and implementing data pipelines, data integration, and data transformation solutions.

*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. "

Machine Learning Engineer - Generative AI

Experience: 3-6 years

& Summary

As a Machine Learning Engineer, you will be a key member of the engineering team responsible for the advanced technical support, development, and deployment of machine learning models, with a special focus on Generative AI. You will be a critical contributor to the design and implementation of end-to-end software lifecycles for AI and machine learning products, ensuring high-quality and scalable solutions. This role requires a deep understanding of both machine learning principles and software engineering best practices, with a proven track record of bringing complex models from research to production, particularly within the Microsoft Azure ecosystem.

Responsibilities

• Lead the design, development, and deployment of production-level machine learning models, with a focus on generative AI models such as Large Language Models (LLMs), Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs).
• Provide advanced technical support for existing AI models, including bug fixes, addressing complex issues, and facilitating model expansion for new business cases.
• Act as a liaison between data scientists, cloud engineers, data engineers, and the business team to diagnose and resolve issues related to model performance and deployment.
• Lead the continuous improvement of support processes and workflows for ML applications.
• Develop, monitor, and maintain end-to-end machine learning pipelines (MLOps) including data preparation, feature engineering, model training, and deployment.
• Utilize Azure AI Foundry to build, test, and deploy cutting-edge AI-driven solutions and generative AI applications.
• Build model performance benchmarking, evaluation, and monitoring capabilities.
• Assess model performance to understand bugs, inefficiencies, and their root cause.
• Document and communicate best practices and troubleshooting procedures to support teams and stakeholders.
• Coordinate and mentor the Machine Learning Engineering team, promoting best practices and code quality.
• Stay current with the latest advancements in AI and machine learning technologies to provide informed support and recommendations.

Required Skills

• Minimum 3-6 years of experience in creating, deploying, monitoring, and maintaining machine learning models in cloud environments.
• At least 2-3 years of hands-on experience in developing and deploying Generative AI models.
• Strong experience working with advanced data analysis on large datasets.
• Hands-on experience with Azure for managing the end-to-end ML lifecycle.
• Proven experience with Azure AI Foundry for building and deploying AI-driven solutions.
• Familiarity with Snowflake and its AI/ML offerings.
• Experience in both front-end(UI/UX) and backend (API development, database management) technologies to build end-to-end solutions.
• Proficiency with data science and data engineering tools such as scikit-learn, XGBoost, pandas, NumPy, SciPy, and Jupyter notebooks.
• Strong understanding of software engineering tools and practices (Python, Git, CI/CD principles).
• Strong understanding of front-end technologies(like Javascript, React, Angular) and backend frameworks (eg Flask, Django)
• Experience with visual analytics platforms (e.g., Tableau, Streamlit).
• Database and other data storage technologies
• System infrastructure knowledge in Linux (Bash, Docker, CRON, Apache Airflow).
• Excellent problem-solving skills and attention to detail.
• Must be able to function independently as well as work in a collaborative team environment.
• Willingness to teach and learn new technologies.

Mandatory skill sets:

Must have rich experience building Operational Data stores, Data marts and Enterprise Data warehouse  -Must have very good SQL skills (Specifically in Oracle, MySQL)  -Should be able to create and execute ETL designs and test cases should be able to write complex SQL queries for testing/analysis depending upon the functional/technical requirement.  -Should have worked on performance optimization, error handling, writing stored procedures, etc. 

Preferred skill sets:

Demonstrate ability to communicate effectively with both Technical & Business stakeholders  -Should have Understanding of data modelling, Data warehousing concepts 

- Should have worked on performance optimization, error handling, writing stored procedures, etc. 

Years of experience required:

4 – 7 yrs

Education qualification:

Btech/MBA/MCA

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

Degrees/Field of Study required: Master of Business Administration, Bachelor of TechnologyDegrees/Field of Study preferred:

Certifications (if blank, certifications not specified)

Required Skills

Extract Transform Load (ETL)

Optional Skills

Accepting Feedback, Accepting Feedback, Active Listening, Agile Scalability, Amazon Web Services (AWS), Apache Airflow, Apache Hadoop, Azure Data Factory, Communication, Data Anonymization, Data Architecture, Database Administration, Database Management System (DBMS), Database Optimization, Database Security Best Practices, Databricks Unified Data Analytics Platform, Data Engineering, Data Engineering Platforms, Data Infrastructure, Data Integration, Data Lake, Data Modeling, Data Pipeline, Data Quality, Data Strategy {+ 22 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

February 5, 2026