Senior AI Platform Engineer

UBS; Greater London; On-site; GBP 90,000 - 140,000; Full time

Job summary

UBS in London seeks an experienced AI/ML engineer to design and deliver production-grade AI systems for front-office platforms. You will build a Unified Knowledge Layer, governance for prompts and skills, and AI-first APIs across core platform services while collaborating with quant developers, DevOps, and platform teams.

You will apply vector search, RAG and agent frameworks to deploy autonomous AI agents for operational issues, incidents and automation workflows in a fast-moving trading

Qualifications

• 5+ years of experience in software engineering, AI engineering or ML engineering.
• Strong Python development skills and production-grade distributed systems.
• Hands-on experience with LLMs, RAG, agent frameworks, prompt engineering, tool calling and context engineering.
• Experience designing APIs, microservices and cloud-native applications.
• Knowledge of vector search, semantic retrieval, indexing strategies and knowledge management architectures.
• Experience with Kubernetes, Docker, GitLab CI/CD and modern software delivery practices.
• Familiarity with observability and monitoring platforms such as Grafana, Loki, Splunk, Prometheus and OpenTelemetry.
• Strong understanding of security, governance and operational controls for enterprise AI systems

Responsibilities

• Design and implement a Unified Knowledge Layer for AI agents, including context management, indexing, retrieval, governance and distribution strategies.
• Evaluate and productionise approaches for context delivery, including MCP architectures, local retrieval models, indexed code repositories and distributed knowledge platforms.
• Establish governance processes for AI skills, prompts and reusable agent capabilities, including approval, versioning and deployment standards.
• Build and maintain a portfolio of AI-first APIs across platform services including our core platform components, KDB, Airflow, configuration services, observability tooling, storage platforms and alerting systems.
• Develop agentic applications, including conversational assistants and autonomous support agents capable of investigating operational issues, generating incident reports and proactively notifying stakeholders.
• Create reusable frameworks that enable engineering teams to deploy autonomous AI agents for use cases such as merge request reviews, remediation workflows and platform support.
• Build AI-enabled analytics capabilities over engineering and operational datasets, ensuring appropriate governance, entitlements and data controls.
• Define evaluation frameworks to measure AI solution accuracy, latency, cost, reliability and operational effectiveness across model and platform changes.
• Collaborate closely with platform engineers, quant-developers, DevOps and operational support teams to embed AI into engineering workflows.

Skills

Python

LLMs

RAG

Agent frameworks

Prompt engineering

Tool calling

Context engineering

APIs

Microservices

Cloud-native

Vector search

Semantic retrieval

Kubernetes

Docker

GitLab CI/CD

Observability

Security & governance

Tools

Kubernetes

Docker

GitLab CI/CD

Job description

Key Responsibilities

Are you excited by the prospect of engineering solutions in across Global Markets? Do you want to design, build and help deliver production systems across the front-office? Are you passionate about building and delivering AI solutions?

Job Type

Full Time

Job Reference #

341932BR

City

London

Key Responsibilities

Are you excited by the prospect of engineering solutions in across Global Markets? Do you want to design, build and help deliver production systems across the front-office? Are you passionate about building and delivering AI solutions?

• Design and implement a Unified Knowledge Layer for AI agents, including context management, indexing, retrieval, governance and distribution strategies.
• Evaluate and productionise approaches for context delivery, including MCP architectures, local retrieval models, indexed code repositories and distributed knowledge platforms.
• Establish governance processes for AI skills, prompts and reusable agent capabilities, including approval, versioning and deployment standards.
• Build and maintain a portfolio of AI-first APIs across platform services including our core platform components, KDB, Airflow, configuration services, observability tooling, storage platforms and alerting systems.
• Develop agentic applications, including conversational assistants and autonomous support agents capable of investigating operational issues, generating incident reports and proactively notifying stakeholders.
• Create reusable frameworks that enable engineering teams to deploy autonomous AI agents for use cases such as merge request reviews, remediation workflows and platform support.
• Build AI-enabled analytics capabilities over engineering and operational datasets, ensuring appropriate governance, entitlements and data controls.
• Define evaluation frameworks to measure AI solution accuracy, latency, cost, reliability and operational effectiveness across model and platform changes.
• Collaborate closely with platform engineers, quant-developers, DevOps and operational support teams to embed AI into engineering workflows.

The team

• A highly technical and innovative engineering team leading automated trading in FX, eRates, eCredit, FX Derivatives, Equity Derivatives, and Cash Equities
• Focused on maximising automation and performance in order to drive eTrading revenues
• Operating in a highly agile manner, releasing to production multiple times per day
• The team is known for being collaborative and diverse, with a mandate to deliver meaningful change

Your Skills And Experience

• An ambitious and intellectually curious individual, keen to combine your technical experience with a clear desire to solve practical problems
• 5+ years' experience in software engineering, AI engineering or machine learning engineering.
• Strong Python development skills and experience building production-grade distributed systems.
• Hands-on experience with LLMs, RAG, agent frameworks, prompt engineering, tool calling and context engineering.
• Experience designing APIs, microservices and cloud-native applications.
• Knowledge of vector search, semantic retrieval, indexing strategies and knowledge management architectures.
• Experience with Kubernetes, Docker, GitLab CI/CD and modern software delivery practices.
• Familiarity with observability and monitoring platforms such as Grafana, Loki, Splunk, Prometheus and OpenTelemetry.
• Strong understanding of security, governance and operational controls for enterprise AI systems

About Us

UBS is a leading and truly global wealth manager and the leading universal bank in Switzerland. We also provide diversified asset management solutions and focused investment banking capabilities. Headquartered in Zurich, Switzerland, UBS is present in more than 50 markets around the globe.

We know that great work is never done alone. That’s why we place collaboration at the heart of everything we do. Because together, we’re more than ourselves. Want to find out more? Visit ubs.com/careers.

Join us

At UBS, we know that it's our people, with their diverse skills, experiences and backgrounds, who drive our ongoing success. We’re dedicated to our craft and passionate about putting our people first, with new challenges, a supportive team, opportunities to grow and flexible working options when possible. Our inclusive culture brings out the best in our employees, wherever they are on their career journey. And we use artificial intelligence (AI) to work smarter and more efficiently. We also recognize that great work is never done alone. That’s why collaboration is at the heart of everything we do. Because together, we’re more than ourselves.

We’re committed to disability inclusion and if you need reasonable accommodation/adjustments throughout our recruitment process, you can always contact us.

Disclaimer / Policy statements

UBS is an Equal Opportunity Employer. We respect and seek to empower each individual and support the diverse cultures, perspectives, skills and experiences within our workforce.