„NatWest Group“ yra Jungtinėje Karalystėje veikianti bankininkystės ir finansinių paslaugų organizacija, aptarnaujanti privačius, verslo ir institucinius klientus. Jos veikla apima bankininkystę, finansavimą ir finansines technologijas, įskaitant verslo bankininkystę, turto valdymą ir rizikos valdymą. Grupė taip pat pabrėžia ryšius su klientais ir bendruomenėmis bei galimybes kurti karjerą jos bankininkystės ir technologijų srityse dirbančiose komandose.
Vice President Machine Learning Engineer at NatWest Group, Bengaluru
Build production generative AI, agentic AI and machine learning solutions.\nDeliver secure, scalable systems using MLOps, cloud engineering and data pipelines.
Aprašymas
- Lead the design and rollout of AI and machine learning solutions using generative AI and traditional ML techniques.
- Serve as a technical expert building and deploying production-ready ML and AI systems.
- Promote strong engineering practices across the team.
- Translate business needs into scalable ML and AI solutions in partnership with data scientists.
- Work with data scientists, engineers, architects and business stakeholders to deliver secure, dependable products.
- Find ways to improve ML and AI platforms, tools and delivery capabilities.
- Build, deploy and maintain production machine learning, generative AI and agentic AI solutions.
- Establish ML engineering and MLOps practices, including CI/CD, automated tests, model monitoring and observability.
- Create scalable data pipelines, feature engineering workflows and reusable platform components.
- Contribute to iterative delivery and continuous improvement in Agile, cross-functional teams.
- Meet engineering, security, governance, risk and regulatory standards across the software and model lifecycle.
- Mentor colleagues and provide technical leadership.
Reikalavimai
- Experience designing, building and supporting production ML and AI solutions in a modern engineering environment.
- An undergraduate or master's degree in a quantitative, engineering or computer science field is preferred, or equivalent practical experience.
- Strong software engineering background, including cloud-native development, data engineering and machine learning operations.
- Ability to provide technical leadership, uphold engineering standards and mentor others.
- Able to manage work independently, take initiative and act proactively.
- Track record of delivering complex ML and AI solutions into production.
- Ability to explain technical concepts to both technical and non-technical audiences.
- Strong software engineering skills, including object-oriented programming, API development, testing frameworks, source control and current development practices.
- Experience developing, deploying and supporting generative AI and agentic AI solutions, including orchestration frameworks, evaluation methods and operational monitoring.
- Experience with traditional machine learning methods.
- Expertise in MLOps, including model deployment, CI/CD, infrastructure as code, observability, model monitoring and performance optimisation.
- Knowledge of Docker and Kubernetes.
- Experience with AWS, Python, SQL and distributed data processing technologies.
- Strong spoken and written communication skills.
- Useful additional experience includes RAG, semantic search, vector databases, knowledge graphs, retrieval optimisation, MCP, tool-calling integrations, LLM evaluation, observability and production monitoring.