AI Engineer, VP
AI Engineer, VP
NatWest GroupThis job is no longer accepting applications
Job Description
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
- 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
- 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
- Strong 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
More Info
Key Skills
embeddings
SQL data modelling
EKS
vector stores
SageMaker
infrastructure as code
Amazon Bedrock
transformer-based models
RAG architectures
About Company
Skills:
Java, Google Cloud, AWS, Enterprise Security, Python, Azure, Iam, LangChain, Go, Langraph, GoogleADK, rbac, A2A protocols, sandboxing, cloud-native DevOps practices
Skills:
Jax, Pytorch, LLM-driven medical agents, contrastive models, clinical NLP, vision–language transformers, multimodal learning, 3D data representations, DICOM pipelines, radiology workflows, Medical Imaging, large-scale training and inference
Skills:
Azure, AWS, Python, Numpy, Pandas, Scikit-learn, Agentic AI frameworks, Data pipelines
Skills:
Keras, AWS, Pytorch, Python, Tensorflow, Azure, Git, Numpy, Pandas, Google Cloud Platform, scikit-learn
Skills:
AI-based retrieval-augmented generation (RAG applications, Python, Keras, Pytorch, Tensorflow, Pandas, Numpy, Microsoft Azure, Agile methodologies and tools, OpenAI, scikit-learn