Level

NatWest Group

Machine Learning Engineer, Vice President

NatWest Group is hiring a Machine Learning Engineer, Vice President in Bengaluru, India. Level rates it ; you can apply on Level.

AI in this role

Lead the design, deployment, and operationalization of production-grade machine learning, Generative AI, and Agentic AI solutions.

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ragai-agentsml-opsai-evaluationmachine-learninggenerative-aimlopscloud-nativefeature-engineering

Join us as a Machine Learning Engineer


  • In this role, you'll drive the design, development and operationalisation of machine learning and AI solutions, enabling the delivery of resilient, scalable and innovative solutions using both Generative AI and traditional Machine Learning techniques
  • Day-to-day, act as a technical expert, building and deploying production-grade ML and AI systems while championing engineering best practices across the team
  • If you are ready for a new challenge and are passionate about turning cutting-edge machine learning and AI capabilities into robust business solutions, this could be the role for you
  • We're offering this role at vice president level

What you’ll do


We're looking for someone who can work with our Data Scientists to understand business requirements and translate them into scalable machine learning and AI solutions. You'll work closely with data scientists, engineers, architects and business stakeholders to deliver reliable and secure products that support our business strategy and customer outcomes.


You'll stay curious about emerging technologies and industry developments, helping to identify opportunities to improve our machine learning and AI platforms, tooling and delivery capabilities.


You’ll also be responsible for:


  • Designing, building, deploying and maintaining machine learning, Generative AI and Agentic AI solutions in production environments
  • Implementing robust ML engineering and MLOps practices, including CI/CD pipelines, automated testing, model monitoring and observability
  • Developing scalable data pipelines, feature engineering processes and reusable platform components to support model deployment and operation
  • Working collaboratively within Agile, multi-disciplinary teams to deliver value through iterative development and continuous improvement
  • Ensuring solutions meet engineering, security, governance, risk and regulatory requirements throughout the software and model lifecycle

The skills you’ll need


To be successful in this role, you'll need experience designing, building and supporting production machine learning and AI solutions within a modern engineering environment. You'll ideally hold an undergraduate or master's degree in a quantitative, engineering or computer science discipline, or demonstrate equivalent practical experience.


You'll also need strong software engineering experience, including cloud-native development, data engineering and machine learning operations. We'll look to you to demonstrate technical leadership, a passion for engineering excellence and the ability to mentor others while continually developing your own skills. Ability to self-manage, take initiative and demonstrate a proactive approach to delivering high-quality engineering outcomes is also required.


You’ll also need:


  • Proven experience delivering complex machine learning and AI solutions into production and communicating technical concepts effectively to both technical and non-technical audiences
  • Strong software engineering skills, including object-oriented programming, API development, testing frameworks, source control and modern development practices
  • Experience developing, deploying and supporting Generative AI and Agentic AI solutions, including orchestration frameworks, evaluation methodologies and operational monitoring – as well as traditional Machine Learning techniques
  • Expertise in MLOps practices, including model deployment, CI/CD, infrastructure as code, observability, model monitoring and performance optimisation. Knowledge of containerisation and orchestration technologies such as Docker and Kubernetes
  • Experience working with cloud technologies such as AWS, alongside strong proficiency in Python, SQL and distributed data processing technologies
  • Effective verbal and written communication skills, with the ability to adapt communication style for different audiences and stakeholder groups
  • Additional skills that would be beneficial: RAG, semantic search and vector database experience, Knowledge graph and retrieval optimisation, MCP and tool-calling integration patterns for AI agents, LLM evaluation, observability and production monitoring

Hours

45

Job Posting Closing Date:

19/10/2026

How we rate this

Machine Learning Engineer, Vice President at NatWest Group rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGAI agentsML OpsAI EvaluationMachine learningGenerative AIMLOpsCloud Native

Also common in machine learning engineer jobs at this level

Pay

Typical machine learning engineer job
$199k

This posting doesn't list pay. Use the typical machine learning engineer figure when pay comes up. See machine learning engineer jobs

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. How do you decide when an AI agent can act on its own versus asking for approval first?
  3. How do you monitor a model once it's live, and how do you know it needs retraining?
  4. How do you decide that one model's output is better than another's for a given task?
  5. Tell me about a project where machine learning was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: RAG, AI agents, ML Ops, AI Evaluation, and Machine learning. 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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