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.

See all jobs on JobTailor

AI Platform Administrator

AI Engineering Team Lead

AI Engineer

AI Developer

AI Engineer – Contract role

AI Developer

Junior AI Engineer

AI Engineer

AI Engineer – Mid Level

Staff AI Engineer