AI Engineer

• Own and run the sprint process day to day, using JIRA as the core tool
• Track and report progress outwards to stakeholders
• Bring your own ideas and ways of working to get the most out of JIRA and agile process generally
• Support the wider team's agile maturity as the platform deployment scales

Agile Project Manager – Banking Platform Deployment

• As the Lead AI/Agent Architect, you own the technical track.
• You will design the agent orchestration model, the tool-use architecture, and the human-in-the-loop protocol, and you will stand up the secure sandbox environment the system runs against.
• You will turn a fragmented, document-heavy operational workflow into a secure, auditable, and extensible agent system, and you will validate exactly where automation ends and expert judgment begins.
• You are accountable for an architecture that processes a live transaction scenario end to end with human intervention only at documented gates, and that extends to adjacent workflows without a rebuild.
• Own the orchestrator agent that holds the deal state machine and routes work to one specialist agent per stage, defining stage handoff protocols and cross-stage integration.
• Design the typed tool layer as the integration seam that binds to sample data in the sandbox and to live systems in production, so the move to production is a connector change rather than a rebuild.
• Define the mandatory approval gates and review checkpoints, ensuring the agent never executes binding legal or financial actions without broker confirmation.
• Own the sandbox data model and test scenarios, and define the security, compliance, and data-governance approach.

Lead AI/Agent Architect

• Per-Stage Agent Build: Implement agent capabilities for individual workflow stages (for example engagement agreement generation, needs-assessment brief, market survey and long-list scoring, tour management, RFP and proposal analytics, lease abstraction, and close-out and invoicing), building to the orchestration model and stage handoff protocols defined by the Lead Architect.
• Tool & Integration Development: Build the typed tools the agents call: template population, market-data query, financial analytics (NER, TI, free rent, NPV), clause and lease-clause extraction, and document assembly.
• Data Pipelines: Build and maintain pipelines against sandbox and representative sample data, including the property availability data source.
• Human-in-the-Loop Implementation: Encode the approval gates and review checkpoints specified by the architect, so binding steps always route through a broker confirmation.
• Quality & End-to-End Testing: Validate components against deal-scenario data and participate in end-to-end testing across all eight stages, checking artifact quality and stage handoff integrity.
• Build-Phase Readiness: Participate in Phase 1 architecture workshops to ensure the build starts on solid foundations.

AI/ML Engineer

• Lead quantitative market research projects from brief through to delivery
• Own and evolve key reporting outputs
• Analyse and interpret data independently and with teams
• Identify opportunities to use AI-led approaches
• Work closely with Senior Brand Managers and wider Marketing teams
• Manage third-party research suppliers
• Proactively spot opportunities for research to shape decisions
• Support non-research colleagues with self-service projects

Market Research and AI Transformation Lead

Machine Learning Engineer

• Provide technical leadership and ship impactful ML-based solutions
• Collaborate with cross-functional product squads including product managers, data scientists, and engineers
• Design and develop advanced real-time Machine Learning models
• Identify and scope impactful opportunities to tackle Financial Crime and Fraud

Lead Machine Learning Scientist, FinCrime

• Build Production-Grade Evaluation Systems: Design and implement comprehensive evaluation frameworks that measure agent performance, track improvements over time, and ensure our AI systems deliver consistent value to customers
• Drive Experimentation-to-Production Pipeline: Own the entire ML lifecycle from prototype to production, building scalable systems that enable rapid iteration while maintaining reliability and performance in customer environments
• Enable Cross-Team ML Integration: Work closely with product teams to seamlessly integrate ML capabilities into customer-facing features, ensuring technical excellence translates into user value and product differentiation
• Optimize AI Agent Performance: Continuously improve our AI agents through systematic experimentation, prompt engineering, and architectural enhancements, measuring success through customer impact and system performance
• Scale ML Infrastructure: Build the foundational ML systems, monitoring, and tooling that will support our growth from startup to scale, ensuring we can deploy new capabilities quickly without compromising quality
• Partner with Engineering Leadership: Collaborate directly with our CTO through regular check-ins and strategic alignment while operating with high autonomy and self-direction in day-to-day execution
• Mentor Through Excellence: Provide natural mentorship to junior ML engineers through code reviews, technical guidance, and sharing practical experience from building production ML systems

• Lead the delivery of highly engaging, interactive, and practical workshops on AI, Python, Prompt Engineering, and automation tools.
• Own delivery quality and metrics for key client cohorts, acting as the Lead Tutor for complex accounts and high-stakes learner groups.
• Guide learners through complex technical exercises, providing coaching, feedback, and support to overcome blockers and apply learning in their roles.
• Run live-coding and interactive automation sessions with confidence, showcasing best practices in using AI tools for rapid prototyping and productivity.
• Author original modular learning assets, including hands-on automation labs, workshop guides, hackathon challenges, and technical toolkits to close specific learner gaps.
• Identify gaps in the existing curriculum based on classroom feedback and iterate on materials to ensure they remain highly relevant and up to date.
• Ensure all learning content is pedagogically sound, accessible to non-technical business professionals, and aligned with Decoded's high delivery standards.
• Maintain a strong practitioner-level understanding of the AI & Automation landscape, particularly low-code/no-code automation tools.

Lead Instructor – AI & Automation

• Research, develop and deploy advanced machine learning solutions for image analysis, image processing, object detection and segmentation.
• Evaluate technical challenges and determine the most effective ML approach, whether developing custom models or adapting state-of-the-art solutions.
• Explore emerging technologies and techniques to continuously improve product capabilities.
• Design, develop and maintain robust software solutions using modern software engineering practices.
• Integrate machine learning algorithms into high-performance desktop applications.
• Contribute to the wider C++ application architecture and codebase.
• Participate in code reviews and contribute to development best practices.
• Profile and optimise model execution for CPU and GPU acceleration.
• Ensure efficient processing of large-scale 3D and 4D imaging datasets.
• Develop scalable and maintainable solutions suitable for production environments.
• Provide technical guidance, mentoring and support to other software engineers and test team members.
• Contribute to technical investigations and architectural decisions.
• Share knowledge and promote engineering excellence across the team.
• Work closely with software engineers, scientists, product managers and domain experts.
• Support product development from concept through to deployment.
• Occasionally travel to other Oxford Instruments sites and customer locations when required.

Senior Machine Learning Software Engineer

• This role sits as part of a multidisciplinary squad, collaborating with other Machine Learning Scientists, Data Scientists, Backend Engineers, Operations specialists, Product managers, and Risk managers.
• You'll play a key role by leveraging your deep experience of developing and deploying advanced Machine Learning models as to automatically and accurately detect suspicious user behaviours while minimising impact to genuine customers and operational costs.
• Adapt quickly and appropriately to changing fraud and financial crime trends, ensuring our detection systems remain performant through time.
• Design machine learning solutions that scale globally.
• The technical approaches you take to solve these problems will be very much in your hands and we’ll strongly encourage and support experimentation and innovation.

Senior Machine Learning Scientist

• Design, build, and ship AI-powered features that improve clinical decision-making, patient outcomes, and operational efficiency.
• Own the end-to-end feature lifecycle: prototype, production code, deployment, and post-launch optimisation, ensuring quality, performance, and measurable impact.
• Work directly with clinicians and customers to turn real-world problems into working software, iterating quickly on live feedback.
• Collaborate with other engineers to keep solutions technically sound, scalable, and maintainable.
• Evaluate emerging AI technologies, prototype rapidly, and define implementation approaches with a bias toward shipping.
• Translate ambiguous opportunities into concrete technical requirements and working systems.
• Creating and maintaining key feature evals to monitor and improve products you have built.
• Use quantitative and qualitative insights, including product analytics, user feedback, and clinical research, to prioritise what to build and validate hypotheses.
• Define and instrument key metrics for feature adoption, effectiveness, and business impact, continuously iterating for optimal outcomes.
• Conduct hands-on user research to identify unmet needs where engineering can deliver significant value.
• Benchmark against competitors and emerging AI trends to keep the product innovative.
• Connect what you build to strategic outcomes such as user retention, operational efficiency, and revenue growth.
• Serve as the technical owner for your verticals, communicating progress and impact to stakeholders across the business.

Forward Deployed AI Engineer

• Frame ambiguous business problems as well-posed modeling, inference, or optimization tasks, and choose methods that fit the data and the decision.
• Design, build, validate, and deploy predictive and decisioning models across areas such as fraud and risk monitoring, customer onboarding and due diligence, pricing, and customer lifetime value.
• Run rigorous experiments and causal analyses, including A/B testing, uplift modeling, and offline evaluation, to measure whether models actually move the outcomes that matter.
• Engineer features and build the data pipelines that feed training and serving, with attention to leakage, reproducibility, and data quality.
• Productionise models with strong attention to validation, backtesting, monitoring, drift detection, and retraining, so performance holds up after launch.
• Work closely with product managers, engineers, and domain experts to identify where modeling creates value and to integrate models into products and operational workflows.
• Apply optimization and operations research methods where decisions, not just predictions, are the goal.
• Contribute to modeling standards, evaluation practices, and reusable tooling across the team.
• Stay current with developments in machine learning and statistics, and apply new methods where they earn their place.

Senior Machine Learning Engineer

• Build AI-powered workflows, assistants, agents and automation systems.
• Apply AI across customer support, CRM, onboarding, claims, renewals, payments, operations and internal tools.
• Work with product and engineering teams to turn manual processes into scalable AI-native systems.
• Build integrations with LLMs, internal data, APIs, documents, knowledge bases and business systems.
• Design evaluation, monitoring and fallback flows so AI outputs are useful, safe and reliable.
• Prototype quickly, test with users or operators, then productionize what works.
• Improve speed, quality and consistency across workflows using AI where it creates real business value.

Applied AI Engineer – Finance Super App

• You will be part of a growing global team, serving as a key point of contact for a wide range of clients and colleagues.
• This is a great opportunity to build multidisciplinary teams through change; you will receive strong support tailored to your experience and personal skills.
• EY is a dynamic place to develop not only your technical knowledge related to tax, but also your professional leadership and business acumen.
• Our market-leading approach combines extensive local compliance and accounting expertise across more than 120 countries with global compliance processes and standardized tax accounting.

AI-Native Software Engineer

• Design and deliver hands-on technical workshops for Tech teams — the kind where participants build and ship working AI agents themselves, not watch someone else do it
• Coach engineers and domain experts through identifying real use cases in their function, scoping them rigorously, and building their first working solutions on KAIT
• Run structured AI opportunity audits within Tech teams: helping teams assess which use cases are quick wins they can deliver themselves, which need Architect-level guidance, and which are not worth pursuing
• Create technical training content covering topics such as RAG (connecting AI to internal knowledge bases), AI agent design, prompt engineering, and API integration — written for a Tech audience, grounded in real OBG scenarios
• Provide floor support during workshop sessions, including live debugging and troubleshooting — guiding participants through solving problems, not solving for them
• Ensure security, data governance, and compliance are embedded into every use case from the start — not treated as a gate at the end
• Upskill teams on OBG’s Technology Release Process so they can self-serve: preparing documentation, completing governance checklists, and meeting production standards without needing hand-holding
• Identify gaps in existing processes, documentation, or governance frameworks and flag them to the relevant owners
• Coach Tech Innovators (domain experts building use cases) through the full lifecycle: from identifying where AI adds genuine value, through scoping and prototyping, to handing off complex builds for Architect-level support where needed

AI Enablement Lead

• Design, build, and deploy end-to-end machine learning pipelines — from data ingestion and feature engineering through to model serving and monitoring in production.
• Own the full lifecycle of ML models: evaluate, iterate, and retire them with the same rigour you bring to building them.
• Collaborate closely with product and engineering teams to frame business problems as machine learning problems, and translate model outputs into product features users actually understand.
• Establish and maintain standards for ML reproducibility, experiment tracking, and model versioning across the Data team.
• Identify opportunities to apply ML across Alto's product suite — surfacing ideas proactively, not waiting to be briefed.
• Work with large, complex datasets drawn from live UK property transactions, ensuring data quality and feature reliability upstream of every model.
• Contribute to a culture of engineering excellence through code reviews, documentation, and knowledge-sharing with data engineers and analysts.

• Build and deliver production-ready AI and Generative AI solutions using LLMs, RAG architectures, and agents
• Implement and maintain retrieval pipelines using embeddings, vector databases, hybrid search, and effective chunking strategies
• Develop proof-of-concepts for emerging AI technologies and assess their production viability
• Write clean, maintainable code following established engineering best practices and quality standards
• Use AI coding assistants such as GitHub Copilot and Claude Code to accelerate development
• Participate in code reviews and architectural discussions to improve code quality and system design
• Work closely with Product and Engineering teams to understand requirements and deliver iterative solutions
• Collaborate with cross-functional partners (Security, Data, Operations) to ensure solutions meet quality, compliance, and scalability requirements
• Communicate technical decisions and tradeoffs clearly with both technical and non-technical stakeholders
• Contribute to improving AI development practices and tooling through feedback and suggestions
• Stay current with AI/ML developments, emerging frameworks, and best practices
• Learn from more experienced engineers and contribute knowledge back to the team
• Participate in capability-building activities and knowledge-sharing sessions
• Build expertise in production AI systems, model evaluation, and optimization techniques

• Design and build production-ready AI/ML systems, with an emphasis on Standard Model LLM-powered product and platform features
• Leverage LLM tooling/APIs such as LangChain and MCP connectors to implement retrieval-augmented generation (RAG), copilot-assistants and agentic workflows
• Partner with Product Management and product teams to translate requirements into AI-powered capabilities that surface directly in user-facing products
• Apply MLOps and LLMOps best practices: monitoring, evaluation, prompt versioning, cost/performance optimization
• Combine traditional AI/ML with modern GenAI approaches to deliver hybrid solutions where appropriate
• Collaborate with Data Engineers to establish a scalable data pipeline that serves structured data shaped for LLM consumption, feature store data for traditional AI, and Trad/GenAI-enhanced insights for internal and customer-facing BI use cases
• Mentor and upskill peers in on core AI/ML and LLMOps practices, raising the overall AI/ML competency of the team
• Stay current with developments in GenAI, LLMOps, generative AI safety frameworks, and evaluate their potential for adoption within the platform

Senior AI Engineer

• Must work from the UK
• Responsible for building the automations and AI integrations that embed AI into operations
• Build, deploy and maintain AI-enabled automations across core systems stack
• Deliver against an internal AI roadmap spanning Customer Operations, Marketing, Finance, Education and Training
• Automate high-volume, manual administrative workflows
• Build AI-enabled workflows in Salesforce, Zendesk, HubSpot, Zuora and Thrive LMS
• Turn the Mission Control data platform into an engine for event-triggered automation
• Reduce operational overhead and improve reporting speed and accuracy
• Build and maintain automation pipelines using n8n, Zapier/Make and direct LLM API integrations (OpenAI/Anthropic)
• Develop API and webhook integrations across Salesforce, Zendesk, HubSpot, Zuora and Thrive LMS
• Implement workflows such as ticket summarisation, tagging, routing and AI triage
• Design and implement RAG pipelines and vector database integrations over internal knowledge
• Develop, test and version prompts; evaluate and improve output quality
• Build event-triggered automations and AI-enabled reporting on Mission Control
• Apply sound engineering standards: error handling, logging and monitoring of automations in production
• Ensure GDPR-compliant, secure handling of customer and company data
• Document solutions clearly and hand over to internal teams for ongoing operation

AI Engineer – Contract

• Map current-state workflows and quantify manual effort and bottlenecks
• Translate findings into clear, prioritised requirements for automation delivery
• Design the taxonomies and knowledge structures that power AI-enabled tools
• Turn scattered documentation into structured, AI-ready knowledge bases
• Audit, clean and restructure existing SOPs, policies and training content for AI consumption, including RAG knowledge bases
• Establish documentation standards: format, ownership and review cadence
• Work with the AI Engineer to ensure knowledge bases serve retrieval pipelines accurately
• Produce SOPs, process maps and RACI documentation where needed
• Support adoption with playbooks and training materials
• Track realised benefits after each automation is deployed

AI Business Analyst, Knowledge Architect – Contract

• Design and Develop AI Platform Services: Build reusable, scalable services that expose GenAI models, knowledge retrieval pipelines, and agent workflows to application teams.
• Knowledge Base Development: Build and maintain knowledge retrieval systems. Process heterogeneous documents like pdfs and build embedding pipelines that extract, chunk and embed into embedding models.
• AI Readiness: Process content from unstructured documents and make it AI ready: Optimize their structure, clear up text ambiguity, and embed rich machine-readable metadata.
• AI Ops, evals and observability: Set up framework for monitoring and evaluating AI output quality (relevance, accuracy, safety, drift, cost) and platform observability (latency, cost, usage).
• Centre of Excellence: Act as a centre of excellence for the whole business in the technical side of AI and LLMs usage, setting best practices and accelerating adoption.
• Governance and Guardrails: Create governance layers for implementing PII redaction, prompt filtering etc.

See all jobs on JobTailor

Agile Project Manager – Banking Platform Deployment

Lead AI/Agent Architect

AI/ML Engineer

Market Research and AI Transformation Lead

Lead Machine Learning Scientist, FinCrime

Machine Learning Engineer

Lead Instructor – AI & Automation

Senior Machine Learning Software Engineer

Senior Machine Learning Scientist

Forward Deployed AI Engineer

Senior Machine Learning Engineer

Applied AI Engineer – Finance Super App

AI-Native Software Engineer

AI Enablement Lead

Senior Machine Learning Engineer

AI Engineer

Senior AI Engineer

AI Engineer – Contract

AI Business Analyst, Knowledge Architect – Contract

Senior AI Engineer