AI Engineer

• Manages AI platforms and environments, including access provisioning, governance controls, and policy enforcement (e.g., DLP, security, and compliance)
• Develops reusable components (e.g., prompts, connectors, APIs, templates) to accelerate AI solution delivery and promote standardization across use cases
• Designs and maintains the foundational AI infrastructure, frameworks, and observability capabilities (telemetry, monitoring, metrics) required for scalable, reliable, and governed AI operations
• Enforces security protocols, data handling rules, and role-based access controls in collaboration with InfoSec and Legal
• Oversees workspace configuration, user provisioning, single sign-on (SSO), and licensing
• Connects AI tools with core enterprise software (like HRIS, CRMs, and document repositories)
• Creates internal training materials, troubleshooting guides, and tracks AI usage metrics to drive value realization
• Tracks platform performance, API error rates, and token consumption to optimize operational costs

AI Platform Administrator

• Lead the planning, execution, and delivery of multiple AI initiatives, ensuring projects meet scope, timelines, quality expectations, and business objectives
• Mentor and support AI Engineers by setting clear direction, providing coaching, and building a high-performing project team
• Partner with stakeholders across product, science, clinical, operations, and engineering to define requirements, priorities, milestones, risks, and success metrics
• Translate stakeholder needs into technical roadmaps, delivery plans, and resourcing strategies for AI-powered products and platforms
• Guide solution design, software development, model integration, testing, deployment, and operational support across the AI project portfolio
• Drive strong AI research and engineering practices across the team
• Coordinate dependencies, remove blockers, and communicate project status, risks, and tradeoffs clearly to leadership and stakeholders
• Balance innovation and experimentation with execution discipline, scalability, reliability, and compliance considerations
• Contribute to hiring, onboarding, team planning, and continuous improvement of delivery processes and engineering standards

AI Engineering Team Lead

• Design, build, and maintain full-stack web applications, including front-end development, serverless or containerised back-ends, APIs, and database storage.
• Take ownership of and accelerate the development of flagship GenAI products, like our internal "Customer Profile," to reduce time-to-value and iterate rapidly based on user feedback.
• Collaborate directly with AI/ML engineers, data scientists, product engineers and product managers in a rapid experimentation and prototyping environment.
• Embrace and drive a culture of full lifecycle ownership, taking responsibility for your applications' design, development, ongoing production support, maintenance, and on-call duties.
• Help evolve our cross-functional operating model, establishing robust, repeatable processes and best practices for building end-to-end AI applications.

• Work directly with teams and customers to identify high-impact opportunities for AI and workflow automation.
• Design, build, and deploy AI agents and intelligent systems into production environments.
• Lead discovery sessions to understand workflows, pain points, and business requirements.
• Translate complex business challenges into practical technical solutions.
• Rapidly prototype ideas and iterate based on real-world feedback.
• Integrate AI solutions with existing systems, applications, and data sources.
• Build reusable frameworks and components that accelerate future AI initiatives.
• Monitor adoption, performance, and effectiveness of deployed solutions.
• Collaborate across technical and non-technical teams to deliver meaningful outcomes.
• Stay current with emerging AI technologies and identify opportunities to apply them.

AI Developer

• Joining Customer Service Transformation Programme, working within the AI & Agents squad.
• The squad owns customer-facing agentic AI on AWS Bedrock with multi-agent collaboration patterns using Claude Sonnet.
• Concurrent work includes domain-specific chat agents going live, additional domain agents being designed, chatbot automation, and Agent WorkSpace AI features (real-time alerts, complaint indicators, Agent Note Taker).

AI Engineer – Contract role

• Design and deploy platform features, data pipelines, automations, and internal tools that improve Allocator's data aggregation, analytics, and reporting workflows
• Use AI coding assistants as core daily tools; we value problem-solving ability and execution speed over memorising syntax
• Build agentic workflows and AI-driven features into our platform and internal tooling, applying LLMs and orchestration frameworks where they deliver real leverage
• Analyse workflows across data collection, fund reporting, and client delivery, identifying improvement opportunities and implementing practical solutions
• Move from concept to prototype to production efficiently. Launch MVPs, track performance, and iterate based on client and internal feedback
• Collaborate with Product, Data, and Client Success teams to understand requirements, demonstrate solutions, and support adoption
• Establish performance baselines, implement changes, and document measured impact for every feature shipped
• Lead discovery sessions with Product and Data teams, documenting success criteria, technical specifications, and trade-offs clearly
• Develop release notes, setup guides, and onboarding materials accessible to non-technical colleagues and clients

• AI & ML Development: support the development of models and AI-powered features across text, document, and structured data use cases.
• LLM Applications: help build and improve LLM-powered workflows, including prompt design, structured outputs, and simple tool use.
• Data & Evaluation: work with real-world datasets, contribute to evaluation pipelines, and help measure system performance.
• Production Code: write clean, maintainable Python and contribute to code that runs in production systems.
• AI-Assisted Development: use tools like Cursor and Claude Code to accelerate development, while learning good engineering practices.
• Collaboration & Learning: work closely with experienced engineers, product managers, and data teams, learning how to ship impactful features.

Junior AI Engineer

• Design, build, and deploy agentic AI solutions
• Prototype and iterate on AI use cases rapidly
• Develop and implement agentic workflows
• Integrate LLMs, APIs, and data pipelines into scalable solutions
• Collaborate with PMs and stakeholders to refine requirements
• Ensure model performance, reliability, and monitoring
• Contribute to best practices in AI engineering and MLOps

• Write clean, well-tested, production-ready code while setting high standards and mentoring engineers in AI design and delivery.
• Own system design, delivery, and evolution; resolve complex performance, scalability, and integration challenges.
• Build and operate reliable infrastructure using practical DevOps and MLOps practices to monitor, optimize, and improve production AI solutions.
• Communicate technical decisions clearly to non-technical stakeholders and ensure smooth user experiences.

AI Engineer – Mid Level

• Own the overall system architecture, understanding how components interact, where dependencies create risk and where production realities challenge theoretical designs.
• Make day-to-day architectural decisions, defining what gets built, how it is structured and the interface contracts between components, while partnering with the Director of AI and Decision Intelligence on major strategic decisions.
• Translate research ideas into production-ready system designs, evaluating integration approaches, engineering effort, trade-offs and delivery sequencing.
• Identify architectural risks early, ensuring short-term decisions do not create long-term constraints or unnecessary technical debt.
• Build prototypes that validate architectural assumptions, integration patterns and scalability requirements, rather than simply proving that a model or idea works in isolation.
• Define clear boundaries and interfaces between learned and non-learned system components, enabling both to evolve independently without introducing instability.
• Guide technical decision-making across the team, ensuring implementation choices remain aligned with the long-term architecture and product vision, even when delivery pressures favour short-term solutions.
• Act as a technical sounding board for complex design and systems challenges, helping the team make pragmatic decisions in areas with significant uncertainty or trade-offs.

Staff AI Engineer

AI Engineer – Contract, Inside IR35

• Design and develop multi-agent prompts and orchestration patterns
• Define how agents, tools, APIs, services, and platform components interact, including requirements, contracts, and integration patterns
• Work alongside platform engineers to define the APIs and services that agents consume and use
• Work closely with product engineers to support user experience and front-end design, including the use of prompt outputs as code
• Own LLM and prompt tooling across the full lifecycle, from prompt management and versioning through evaluation, testing, and optimisation
• Design requirements for downstream and supporting services that enable agent execution, workflow control, observability, and integration
• Lead the research, evaluation, and selection of frameworks for agent orchestration, tool use, evaluation, observability, and deployment
• Perform model selection based on use case fit, quality, latency, scalability, and cost
• Design and implement tools and function-calling patterns for agent-based systems
• Carry out prompt tuning and iterative optimisation of agent behaviour and output quality
• Monitor, debug, and continuously tune agents in development and production
• Design for performance, scalability, reliability, usability, maintainability, and cost efficiency
• Define and execute testing strategies for LLM agents and platform-level AI workflows
• Integration test agents end to end with other parts of the platform to ensure reliability across workflows, services, and user-facing experiences
• Build proofs of concept and prototypes to validate architectures, frameworks, and new platform capabilities
• Produce end-to-end solution designs for AI platform components and services
• Use AI to improve software development , including prototyping, implementation, testing, debugging, and engineering productivity
• Collaborate across engineering and product teams to translate business and technical goals into robust platform capabilities

• Lead the development of a new AI-powered mental health product (initially audio-focused)
• Take full ownership of the project from early-stage scoping through execution
• Collaborate with clinicians, designers, and product stakeholders to shape the solution
• Architect and build scalable backend systems and APIs
• Contribute to (and potentially lead) frontend/mobile application development
• Establish engineering best practices, including AI-native workflows
• Make key technical and product decisions in an ambiguous, fast-moving environment
• Mentor and help build the team as the project scales

Founding – Lead AI Engineer, Mental Health Tech

• Architect and lead the implementation of multi-agent systems using Google AI SDKs (Vertex AI Agent Builder), LangGraph, CrewAI, and other emerging orchestration frameworks.
• Design and build stateful, tool-augmented agents capable of advanced reasoning, long-term planning, and autonomous execution.
• Develop and document agent orchestration patterns including planner-executor, supervisor-worker, and hierarchical agent structures.
• Implement sophisticated memory systems (short-term, long-term, and cross-session contextual memory).
• Enable seamless cross-agent communication and multi-modal coordination.
• Lead the delivery of production-grade LLM applications: RAG pipelines, specialised agents, and developer copilots.
• Integrate diverse tools, enterprise APIs, and legacy systems into agentic workflows.
• Design robust system prompts, dynamic routing logic, and AI guardrails using Vertex AI Model Garden or Azure AI Studio.
• Drive optimisation of AI workflows for latency, token cost, and output quality.
• Develop and own reusable AI microservices, agent frameworks, and standardised APIs.
• Contribute to core AI platform capabilities including model routing, centralised observability, and safety filters.
• Define and enforce engineering standards and best practices for AI development across the team.
• Deploy and manage agent-based systems on GCP, Azure, and/or AWS using Docker, Kubernetes (GKE/AKS/EKS), and Cloud Run.
• Implement comprehensive monitoring and observability using Vertex AI Inspector, LangSmith, or Azure Monitor.
• Drive incident response and post-mortems for production AI system failures.
• Act as a technical lead on key AI engineering workstreams, shaping architecture and approach.
• Mentor and support more junior AI engineers through code review, design discussions, and pair programming.
• Collaborate with Principal AI Engineer and cross-functional teams (data, product, delivery) to align AI engineering with business outcomes.
• Stay at the forefront of the rapidly evolving agentic AI landscape and bring new approaches into the team.

Senior AI Engineer

• Own the full software development lifecycle (SDLC) for the SEA platform
• Lead hands-on design and code review across microservices, APIs, and AI-integrated application layers
• Drive the architecture of cloud-native applications on Azure
• Architect and oversee the engineering of LLM-powered applications
• Build robust MLOps practices
• Implement responsible AI engineering guardrails
• Define and own the application development standards across the engineering organization
• Build, lead, and grow a world-class engineering organization distributed across multiple geographies
• Define the multi-year engineering roadmap for SEA and Digital Workplace platforms
• Serve as the primary technical voice in executive and client conversations

Senior Director, Global Head of Solution Development – AI Platforms

• Building production-grade AI applications and turning complex business challenges into practical, scalable solutions.
• Working with C#/.NET, Azure and modern LLM technologies.
• Collaborating with product, engineering, QA, UX and business stakeholders.
• Raising AI engineering capability across the wider technology team through coaching, practical standards and technical leadership.
• Shipping meaningful AI-enabled products in short iterations, learn from real usage and continuously improve quality.

• Lead the Advanced AI Engineering team, reporting to the Group Director of Data & AI Architecture
• Define and lead the Group’s Data and AI technology strategy and incubate new AI‑driven products and capabilities
• Evaluate emerging AI technologies, build and deliver MVPs, and transition into engineering at scale
• Create industry‑leading AI‑powered products and services that drive customer engagement and service excellence
• Solve critical business and technology challenges and ensure high‑quality conversational AI delivery
• Shape technical direction, driving applied research and prototyping, and collaborating with senior collaborators and engineering teams.

Head of Advanced AI Engineering

• Design and implement scalable, reliable AI agents and copilots using modern LLM frameworks. Define patterns for orchestration, tool usage, memory, and human-in-the-loop workflows. Provide technical governance and best practices for agent-based systems.
• Strong experience working with large language models (e.g. OpenAI, Azure OpenAI), prompt engineering, embeddings, vector databases, and retrieval-augmented generation (RAG). Ability to evaluate and optimise model performance for real-world use cases.
• Build agents that integrate with internal systems (APIs, databases, SaaS tools such as Microsoft 365, Notion, etc.) to automate workflows and enhance productivity.
• Design intuitive copilot experiences that augment users rather than replace them, with a focus on usability, trust, and explainability.
• As a senior member of a growing team, support other engineers through mentoring, documentation, and establishing best practices in AI engineering.
• Define metrics and evaluation frameworks for agent performance, reliability, and safety. Continuously iterate based on feedback and usage data.
• Experience deploying AI systems into production environments, including monitoring, observability, cost control, and lifecycle management of models and agents.
• Experience of working within a project team in an Agile manner, collaborating closely with stakeholders to iteratively deliver value.
• Ensure AI solutions adhere to security, privacy, and data governance standards. Awareness of risks such as hallucination, data leakage, and misuse, with mitigation strategies in place.
• Liaise with the relevant Business Analyst, Quality Assurance Team, Security Team, Infrastructure Team and stakeholders on matters as required.

• Assess learner project submissions against defined criteria and marking rubrics.
• Provide clear, constructive, and developmental written feedback on all assigned assessments.
• Apply consistent grading standards aligned with programme calibration and moderation guidelines.
• Collaborate with tutors and delivery team members to support marking consistency across cohorts.
• Participate in calibration and moderation activities as required during each marking period.
• Manage allocated marking workloads to meet agreed turnaround times and deadlines.
• Work collaboratively with learner support teams to ensure feedback enables learner progression.
• Maintain accurate records of assessment outcomes in relevant learning systems.
• Providing further async-specific feedback to learners when requested, post mark & feedback

AI Leadership Programme Assessor, Marker – Contract, PhD Level

• Build AI on top of our cloud closing agent pronto
• Create connectors and integrations that make company data available to AI systems (Google Workspace, Slack, Jira, GitHub, Snowflake, Confluence and more)
• Build and maintain knowledge base pipelines, MCP integrations and API connections that power AI tooling across the business
• Work with security and data governance requirements to ensure integrations are safe and appropriate
• Support internal teams to create their own AI-powered data sources, automated workflows and internal tools using rapid app builder tools
• Build templates, guardrails and building blocks that make it easy for non-engineers to experiment safely
• Contribute to our internal automation platform using tools like AWS Bedrock, n8n and custom-built solutions
• Work closely with the PM and engineering lead to identify the highest-leverage opportunities
• Ship quickly, measure outcomes (time saved, errors reduced, adoption) and iterate based on what you learn
• Stay curious about emerging tools and techniques — and apply them where they'll genuinely make a difference to our engineering output

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