Machine Learning Engineer, VP

NatWest Group; Bengaluru; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

NatWest Group is seeking a VP-level Machine Learning Engineer in Bengaluru to design and deploy advanced ML products, automate production pipelines, and lead cross‑functional teams across analytics and business units.

The role emphasizes productionisation, model monitoring, and collaboration with stakeholders to align ML solutions with business strategy. Strong CI/CD, cloud, and leadership skills are essential.

Qualifications

• Academic background in a STEM discipline (e.g., Mathematics, Physics, Engineering or Computer Science).
• Ten years of experience with machine learning on large datasets.
• Experience implementing and sustaining production ML pipelines with CI/CD tools (TeamCity, CodeDeploy).
• Proven ability to coach others and work across cross‑functional teams.

Responsibilities

• Design and develop advanced ML products powering customer solutions.
• Codify and automate production of ML models and optimise pipelines.
• Lead projects and guide multidisciplinary teams in an Agile environment.

Skills

Python

TensorFlow

PyTorch

CI/CD

Docker

AWS

Google Cloud

Azure

MLOps

LLMOps

Education

STEM degree

Tools

TeamCity

CodeDeploy

Git

Job description

• In this role, you’ll be driving and embedding the deployment, automation, maintenance and monitoring of machine learning models and algorithms
• Day-to-day, you’ll make sure that models and algorithms work effectively in a production environment while promoting data literacy education with business stakeholders
• If you see opportunities where others see challenges, you’ll find that this solutions-driven role will be your chance to solve new problems and enjoy excellent career development
• We're offering this role at vice president level

Join us as a Machine Learning Engineer

What you’ll do

Your daily responsibilities will include you collaborating with colleagues to design and develop advanced machine learning products which power our group for our customers. You’ll also codify and automate complex machine learning model productions, including pipeline optimisation.

We’ll expect you to transform advanced data science prototypes and apply machine learning algorithms and tools. You’ll also plan, manage, and deliver larger or complex projects, involving a variety of colleagues and teams across our business.

You’ll Also Be Responsible For

• Understanding the complex requirements and needs of business stakeholders, developing good relationships and how machine learning solutions can support our business strategy
• Working with colleagues to productionise machine learning models, including pipeline design and development and testing and deployment, so the original intent is carried over to production
• Creating frameworks to ensure robust monitoring of machine learning models within a production environment, making sure they deliver quality and performance
• Understanding and addressing any shortfalls, for instance, through retraining
• Leading direct reports and wider teams in an Agile way within multi-disciplinary data and analytics teams to achieve agreed project and Scrum outcomes

The skills you’ll need

To be successful in this role, you’ll have an academic background in a STEM discipline, like Mathematics, Physics, Engineering or Computer Science. You’ll need overall ten years of experience with machine learning on large datasets and an understanding of machine learning approaches and algorithms.

Alongside this, you’ll have experience of building, testing, supporting and deploying machine learning models into a production environment, using modern CI/CD tools, like TeamCity and CodeDeploy. You’ll also have good communication skills to engage with a wide range of stakeholders.

You’ll Also Need

• Experience of coaching others
• Experience of using programming and scripting languages, such as Python and relevant libraries along with machine learning framework such as Tensorflow and Pytorch
• Experience with AWS, Google cloud platform, or Azure for deploying machine learning models
• Strong understanding of CI/CD pipelines, version control such Git, and containerization such as Docker
• Knowledge of various machine learning algorithms, MLOps,LLMOps and familiarity with concepts such as overfitting and model evaluation metrics