We’re looking for a highly skilled Forward Deployed AI Engineer to join our Health, Wealth & Career (HWC) segment—where you’ll design and deliver cutting-edge AI solutions that solve complex, real-world business challenges and shape the future of work.

This is a hands-on, high-impact role where you’ll move beyond traditional development—partnering closely with stakeholders to translate ambiguous needs into scalable, AI-powered solutions that drive measurable value. You’ll own the full lifecycle—from architecture and build through to deployment and adoption—embedding AI into real workflows, integrating enterprise systems, and ensuring solutions are secure, reliable, and truly user-centric. Sitting close to the business, you’ll continuously refine solutions to maximise impact, efficiency, and adoption.

Why join us?

• At WTW, you’ll be part of our Health, Wealth & Career (HWC) segment—where we use data, technology, and deep expertise to shape the future of work.
• You’ll collaborate across multiple lines of business—such as Retirement, Health & Benefits, and Workforce Strategy—gaining exposure to diverse challenges and opportunities.
• As the role evolves, you’ll also help deliver innovative, AI-driven solutions directly to external clients, turning complex problems into scalable outcomes.
• Together, we’re creating more resilient, high-performing organizations—enhancing wellbeing, enabling better decisions, and driving sustainable business success at scale.
• This is a hybrid role, with the flexibility of remote working alongside occasional time in the office to build strong relationships and collaborate effectively with stakeholders.

The Role
This position is ideal for an experienced Staff Engineer, or Technical Lead with a strong enterprise engineering background and a passion for applying it to AI-enabled systems. You’ll bring deep expertise across modern full-stack technologies (.NET, Azure, SQL, React/Angular), along with experience in distributed systems, observability, and AI tooling such as LLMs, retrieval pipelines, agentic workflows, and platforms such as Anthropic Claude. Experience designing and deploying AI agents, leveraging Model Context Protocol (MCP), and orchestrating enterprise-grade AI solutions will be highly valued.

Acting as a bridge between business and technology, you’ll work across product, data science, architecture, and engineering teams, mentoring others, resolving production challenges, and scaling prototypes into robust, enterprise-grade solutions that deliver measurable business impact.

• AI solution delivery: Design and build AI-enabled applications, copilots, agents, extraction pipelines, prediction interfaces, and decision-support tools using foundation models, retrieval-augmented generation, structured outputs, and orchestration frameworks.
• Forward deployed problem solving: Work directly with business teams, product owners, clients, or operational users to understand real workflows, constraints, data quality issues, and adoption barriers, then translate these into working technical solutions.
• LLM and agent engineering: Build and tune LLM workflows, prompt strategies, schema-driven extraction, tool-calling patterns, agent orchestration, evaluation loops, and human-in-the-loop controls.
• Enterprise integration: Integrate AI solutions with enterprise systems, APIs, data platforms, document repositories, workflow tools, observability platforms, and identity and access management services.
• Production engineering: Ensure AI solutions meet enterprise standards for reliability, scalability, latency, maintainability, cost control, logging, monitoring, and operational support.
• Evaluation and quality assurance: Create evaluation datasets, test harnesses, validation tools, regression checks, and quality review workflows to measure accuracy, extraction quality, hallucination risk, and business usefulness.
• Architecture and technical leadership: Define solution architecture, engineering standards, reusable patterns, and implementation approaches for AI-enabled platforms and services.
• Data and knowledge readiness: Work with engineering, data, and business teams to prepare structured and unstructured data, improve metadata, design retrieval strategies, and identify gaps in source content.
• Security, privacy, and governance: Embed access controls, audit logging, data protection, responsible AI controls, security review, and compliance requirements into the AI delivery lifecycle.
• Adoption and enablement: Support users through demos, pilots, training, feedback loops, documentation, and iterative improvement so that deployed AI solutions create measurable business value.

Qualifications

Qualifications

As a Graduate Full Stack Engineer, you’ll join a single squad and contribute across both frontend and backend under the guidance of more experienced engineers. The role exists to learn fast and ship small, well-tested changes that help our customers.

This role is ideal for someone who has recently graduated from a leading university and is excited about applying modern software engineering practices to real business problems. You may not have extensive industry experience yet, but you have demonstrated your capabilities through academic projects, internships, hackathons, research, open-source contributions, or personal projects.

You’ll build your skills in Spring Boot microservices (Kotlin/Java) and modern web technologies (React/TypeScript), working with containerised services on Azure Container Apps and web applications deployed via Azure Static Web Apps.

You’ll learn and apply our practices in testing, observability, CI/CD, security, performance, and accessibility, ensuring your changes meet the standards expected of a UK bank. You’ll work closely with experienced engineers, Product Managers, and Designers: writing code, raising and responding to code reviews, pairing with colleagues, asking questions early, and using GenAI tools (e.g. GitHub Copilot, Claude Code) responsibly and under guidance.

Principal Accountabilities:

Full Stack Delivery

• Deliver scoped pieces of work end-to-end across the full stack, with support from more experienced engineers.
• Build backend services as Kotlin/Java Spring Boot microservices, containerised on Azure Container Apps and exposing REST/OpenAPI APIs that integrate with other services and data stores.
• Build frontend features in React/TypeScript using shared design system components and agreed architectural patterns (e.g. Nx, micro frontends), deployed on Azure Static Web Apps.

Quality, Security & Accessibility

• Write automated tests for the changes you make and help improve the reliability of the squad’s CI pipelines and test suites.
• Add logs, metrics, and traces to the services you work on, and use the squad’s dashboards and alerts to understand how your changes behave in production.
• Apply secure coding basics (e.g. OWASP fundamentals) and follow agreed patterns for logging, audit trails, and the handling of customer data.
• Build user-facing features that meet accessibility standards (WCAG-aware).

Product Development

• Work closely with Product Managers and Designers in your squad to understand the intent behind the work and flag questions or issues early.
• Rapidly prototype ideas and iterate based on feedback and real-world usage.
• Contribute to the end-to-end delivery of features from concept through to production, leaving the codebase in better shape than you found it.

Learning & Innovation

• Read and apply the squad’s Architecture Decision Records (ADRs) and wider engineering standards, asking for guidance where the context is unclear.
• Use GitHub Copilot/Claude Code as your primary assistive tools under guidance, demonstrating full understanding of all code you commit and validating AI-generated output before review.
• Take ownership of your own development, acting on feedback from code reviews, pairing sessions, and one-to-ones with your line manager.
• Stay current with developments in software engineering and AI-assisted delivery and contribute ideas in technical discussions across the team.

Personal Attributes & Experience

We strongly value:

• Curiosity and a genuine passion for technology.
• A builder mindset with a desire to create products that people use.
• Ownership and accountability.
• Strong problem-solving ability and analytical thinking.
• A willingness to learn quickly and embrace feedback.
• Excellent communication and collaboration skills, including a readiness to pick up tasks outside a specialism.
• Comfort working in fast-moving and evolving environments.

Skills & Experience

Essential:

• Bachelor’s or master’s degree in computer science, Software Engineering, Mathematics, or a related field from a reputable university.

Working at Allica Bank

At Allica Bank we want to ensure our employees have the right tools and environment in which to succeed in their role and in support of our customers.

Our employees are at the heart of everything we do, so our benefits are designed with you in mind:

• Full onboarding support and continued development opportunities
• Options for flexible working
• Regular social activities
• Pension contributions
• Discretionary bonus scheme
• Private health cover
• Life assurance
• Family friendly policies including enhanced Maternity & Paternity leave

Lead Analytics Engineer |📍London – Hybrid (1–2 days per week) | 💰Competitive Salary + Benefits

About the Role

At Moonpig Group, data powers how we create personalised experiences, make better decisions, and drive growth. As a Lead Analytics Engineer, you’ll play a key role in shaping how data is modelled, governed, and consumed across the business.

Operating at a business domain level, you’ll combine deep technical expertise with commercial thinking to define, prioritise, and deliver analytics engineering solutions that enable scalable, trusted, and actionable data. You’ll work closely with stakeholders across Product, Marketing, Finance, Engineering, Data Science, and the wider Data team, helping turn complex challenges into high-impact data products.

As a technical leader within Analytics Engineering, you’ll help set standards, mentor others, and influence the future direction of our data platform. This is an opportunity to make a meaningful impact on how Moonpig uses data to innovate, personalise customer experiences, and scale effectively.