Closing date for applications: 01/08/2026

Location Bangalore, India

Job typePermanent | Contract typeFull Time

#R-00282021

Join our digital revolution in NatWest Digital X

In everything we do, we work to one aim. To make digital experiences which are effortless and secure.

So we organise ourselves around three principles: engineer, protect, and operate. We engineer simple solutions, we protect our customers, and we operate smarter.

Job description

This role is based in India and as such all normal working days must be carried out in India.

Join us as a AI Engineer :

- Build and lead a team that ships production generative and agentic AI systems used by millions of customers and colleagues — solving problems that don't yet have a playbook
- Combine strong people leadership with deep, hands-on technical expertise, staying close to the detail while building a high-performing, engaged, and continuously improving team
- Have the autonomy to choose the right tools, frontier models, and architectures for the job, and the scale to see your work make a real-world impact
- We are offering this role at vice president level

What you'll do

- Lead and line-manage a team of AI engineers: setting objectives, managing performance, coaching and developing careers, and building an inclusive, high-trust culture
- Own the technical vision and roadmap for the team's AI systems, and make the architectural decisions that shape how we build, evaluate, and safely operate LLM-powered applications
- Stay hands-on, contributing to design, code, and reviews, and setting the bar for engineering quality across multi-agent workflows, Retrieval-Augmented Generation (RAG) pipelines, and LLM integrations
- Design agent-to-agent communication frameworks, including structured messaging, shared state, coordination protocols,



and failure handling
- Architect and optimise RAG pipelines, covering document chunking, embedding generation, vector storage, retrieval evaluation, ranking, and freshness handling
- Design and own the data pipelines that feed AI systems — ingestion, transformation, and feature/embedding preparation — built for reliability, data quality, and lineage
- Build and operate orchestrated, scheduled workflows using tools such as Apache Airflow, with monitoring, retries, and clear failure handling
- Leverage cloud data platforms such as Snowflake (alongside AWS data services) for scalable storage, transformation, and analytics that underpin AI and ML workloads
- Establish guardrails, observability, and safety mechanisms across the team's systems, including logging, tracing, evaluations, fallback logic, and mitigation of prompt injection and data-exfiltration risks.
- Drive optimisation for low latency, reliability, throughput, and cost across production AI workloads on AWS.
- Integrate and orchestrate a range of frontier LLM providers, balancing capability, cost, latency, and risk, and designing for portability across models and providers
- Partner with senior stakeholders across product, data science, platform engineering, architecture, and risk and compliance to align delivery with business priorities and financial-services obligations
- Champion robust engineering practices, including testing, version control, CI/CD,



and infrastructure as code, and represent the team in governance, model-risk, and architectural forums

The skills you'll need

- Deep, hands-on experience designing and shipping production AI/ML or generative AI systems at scale — in big tech, a high-growth startup, a regulated industry, or anywhere the stakes and complexity were real
- Robust proficiency in Python, with an async-first approach to building agent workflows and API integrations
- Practical experience integrating frontier LLM providers such as OpenAI, Anthropic, and others and agent frameworks such as LangGraph or LangChain
- Solid understanding of RAG architectures, embeddings, and vector stores
- Strong data engineering skills: designing robust data pipelines, with hands-on experience of workflow orchestration tools such as Apache Airflow
- Experience with modern cloud data platforms such as Snowflake, including SQL, data modelling, and building performant, cost-aware transformations
- Proven experience building and operating cloud-native AI services on AWS (e.g. Amazon Bedrock, SageMaker, ECS/EKS, Lambda), using Docker, Kubernetes, and infrastructure as code
- Experience implementing AI guardrails, observability, evaluation, and safety constraints for production systems
- A strong grasp of NLP and transformer-based models, with sound ML and statistics fundamentals
- A pragmatic approach to data security, model risk, and responsible AI — or the curiosity and rigour to pick it up quickly

Welcome to our Bangalore hub

Located at Taurus 4, Bagmane Constellation Business Park, our new office stands as a state-of-the-art hub for innovation and collaboration.

Key facts:

- A 30 acre campus
- Space for 6,000 colleagues
- Opened in 2025

📌 AI Engineer, VP (Bengaluru)
🏢 National Westminster Bank
📍 Bengaluru