Senior AI Engineer - FTC

Partner with the AI Product Owner during discovery activities to assess feasibility, shape solution options, and contribute to product roadmaps and prioritisation.

Translate business challenges into scalable technical solutions through MVPs, proofs of concept, and production-ready implementations.

Contribute to product vision, user stories, acceptance criteria, and technical architecture decisions.

Support experimentation and rapid innovation while maintaining engineering quality and governance standards.

Design, develop, deploy, and optimise AI-powered applications, APIs, copilots, agents, and intelligent automation solutions.

Develop Retrieval-Augmented Generation (RAG) architectures, prompt engineering frameworks, vector search solutions, and enterprise knowledge retrieval capabilities.

Integrate Large Language Models (LLMs) into enterprise applications, business processes, and engineering workflows.

Build and maintain agentic AI solutions using orchestration frameworks such as LangChain, Semantic Kernel, and equivalent technologies.

Evaluate emerging AI technologies and recommend appropriate adoption strategies.

Implement evaluation frameworks and guardrails to measure model quality, safety, reliability, and business effectiveness.

Apply modern software engineering principles including automated testing, source control, CI/CD, infrastructure-as-code, observability, resilience, and security-by-design.

Build scalable cloud-native applications, APIs, microservices, and data pipelines using modern engineering frameworks and patterns.

Collaborate with platform engineering teams to enable AI capabilities across enterprise platforms and developer ecosystems.

Develop reusable frameworks, libraries, patterns, and standards to accelerate AI adoption across engineering teams.

Contribute to AI-assisted software development practices and developer productivity initiatives.

Implement MLOps practices supporting model lifecycle management, deployment automation, testing, monitoring, and continuous improvement.

Build and maintain observability, telemetry, and analytics capabilities for AI solutions.

Monitor model performance, usage patterns, and business outcomes using defined KPIs and OKRs.

Implement model evaluation, drift detection, performance monitoring, and AI safety controls.

Investigate and resolve production issues to ensure reliability, resilience, and operational effectiveness.

Collaborate with Data Engineering teams to prepare, manage, and govern high-quality datasets for AI solutions.

Ensure all AI solutions comply with enterprise security, governance, privacy, risk, regulatory, and responsible AI requirements.

Support explainability, auditability, lineage, and transparency requirements for AI systems.

Develop and implement AI guardrails and governance controls throughout the full AI lifecycle.

Work with data governance and cataloguing platforms such as Microsoft Purview, Databricks Unity Catalog, or equivalent technologies.

Build AI solutions that deliver measurable business outcomes and support KPI and OKR definition and tracking.

Develop dashboards, measurement frameworks, and reporting mechanisms to quantify business value and adoption.

Support training, user enablement, go-live activities, and change adoption initiatives.

Gather user feedback and continuously improve AI products through iterative enhancement.

Partner with Product Owners, Architects, Business Analysts, Data Engineers, Risk teams, and operational stakeholders to solve complex business challenges.

Provide technical leadership and mentoring to engineers adopting AI capabilities.

Contribute to AI communities of practice, engineering standards, and capability development initiatives.

Promote innovation, experimentation, and continuous improvement across engineering teams.

Share knowledge, best practices, and lessons learned to foster organisational AI capability growth.

Strong hands-on experience in Machine Learning, Generative AI, Large Language Models (LLMs), Agentic AI, and Decision Intelligence systems.

Experience designing and implementing: Retrieval-Augmented Generation (RAG) Prompt engineering frameworks AI agents and copilots Vector databases and embeddings Enterprise search and knowledge retrieval systems

Retrieval-Augmented Generation (RAG)

Prompt engineering frameworks

AI agents and copilots

Vector databases and embeddings

Enterprise search and knowledge retrieval systems

Strong understanding of model evaluation, model selection, guardrail design, responsible AI, and AI governance.

Strong software engineering experience using Python, Java, C#, or similar languages.

Experience building production-grade applications, APIs, and cloud-native services.

Experience applying automated testing, CI/CD, infrastructure-as-code, and DevSecOps practices.

Knowledge of containerisation and orchestration technologies such as Docker and Kubernetes.

Understanding of enterprise integration patterns and API-first architectures.

Experience with one or more of:

AI APIs, including OpenAI, Anthropic, Google Gemini

Azure tooling, including Microsoft Foundry, Azure AI Services, Azure Machine Learning

Databricks

LLM and ML Frameworks, such as LangChain, LangGraph, Semantic Kernel, PyTorch, TensorFlow

M365 Copilot including Copilot Studio

GitHub Enterprise tooling, including GitHub Copilot

Model Hosting, including Hugging Face

Azure DevOps

Experience with cloud platforms, preferably Microsoft Azure.

Experience delivering solutions within regulated environments such as insurance, pensions, investments, banking, or asset management.

Understanding of governance, operational resilience, security, auditability, risk management, and data privacy requirements.

Experience building solutions that meet regulatory and responsible AI expectations.

Strong analytical and problem-solving capability.

Excellent communication and stakeholder management skills.

Ability to explain complex technical concepts to both technical and non-technical audiences.

Product- and outcome-oriented mindset focused on delivering measurable value.

Collaborative approach with experience working in agile, cross-functional teams.

Passion for innovation, continuous learning, and engineering excellence.

Experience implementing enterprise-scale AI solutions in production environments.

Experience with AI observability tooling and model monitoring platforms.

Knowledge of service design and customer-centred product development.

Experience with Power Platform and AI-enabled automation solutions.

Familiarity with Microsoft Purview, Unity Catalog, or equivalent governance platforms.

Knowledge of AI ethics, regulatory requirements, and responsible AI practices.

Delivery of secure, scalable, and measurable AI solutions that achieve business outcomes.

Successful deployment of AI capabilities into production with high levels of reliability, observability, and governance.

Increased organisational productivity through responsible AI adoption.

Demonstrable business value measured through defined KPIs and OKRs.

Strong compliance with governance, privacy, security, and regulatory standards.

Growth of enterprise AI capability through mentoring, reusable assets, and technical leadership.