AI Engineer

• 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.

Machine Learning Engineer

• Attend technical and IT meetings to identify inefficiencies and automation opportunities
• Translate business pain points into clear AI/ML use cases and delivery plans
• Design and build LLM- and ML-based solutions to improve internal processes
• Deploy automation tools and AI agents that enhance quality, speed, and consistency
• Collaborate with architects and stakeholders, owning delivery of well-scoped initiatives

AI/ML Engineer – SC Cleared

• 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

Senior AI Engineer, AI Engineering

• Own and ship ML in production: take ideas from R&D to robust, maintainable deployments, often onto edge or embedded hardware.
• End-to-end ownership: data collection/curation, feature engineering, model training, evaluation, deployment, monitoring, and iteration.
• Technical leadership: set direction, guide design, perform reviews, mentor teammates, and raise the engineering bar.
• MLOps/LLMOps: CI/CD for models, containerisation/orchestration, experiment tracking and registry, model evaluation pipelines, safety guardrails, canaries, and performance monitoring.
• Cross-team collaboration: partner with software, systems, and product colleagues; simplify complex topics for other disciplines and customers.
• Data foundations: establish pragmatic data pipelines (batch/stream) that make curation, provenance, and reproducibility first-class.

Senior Machine Learning Engineer

• Build production AI systems: Design and implement the full stack, from FastAPI endpoints that handle requests, to training pipelines that process data, to inference services that serve predictions. You'll own the architecture, not just the model weights.
• Train and deploy our DSLM: Fine-tune models using Unsloth/Axolotl, but more importantly, build the robust infrastructure around it - data pipelines that feed training, evaluation frameworks that catch regressions, deployment systems that handle failover. Make it production-grade.
• Integrate ML into our backend: We use FastAPI, PydanticAI, FastMCP, Memgraph. You'll extend these systems with ML capabilities, not as a separate "ML service" but as a natural part of our backend architecture. Clean abstractions, proper error handling, observability.
• Own inference performance: Get models running fast, whether that's vLLM deployment, quantization strategies, batching optimizations, or caching. Hit our <200ms latency targets through engineering, not just throwing bigger GPUs at it.
• Shape Project Genome's foundation: Work with our Principal Engineer to architect how we ingest, process, and learn from global supply chain data. This is systems design as much as ML with data pipelines, graph databases, incremental learning strategies being just as important.
• Mentor through code review and pairing: Raise the bar on code quality, testing, and production practices across the team. Teach mid and junior engineers how to build ML systems that don't fall over.

See all jobs on JobTailor

Senior AI Engineer

Senior Director, Global Head of Solution Development – AI Platforms

Senior AI Engineer

Head of Advanced AI Engineering

AI Engineer

AI/ML Engineer – SC Cleared