Aveni

• Working closely with clients to understand strategic objectives, operational challenges and implementation requirements • Translating client needs into clearly defined deliverables for engineering and product teams • Supporting implementation planning, including milestones, timelines and stakeholder updates • Acting as a key point of contact throughout the client implementation lifecycle • Managing and triaging technical issues, feedback and readiness activities during deployment • Supporting testing and quality assurance activities to ensure successful solution delivery • Providing post-implementation support during agreed support periods and peak delivery phases • Collaborating cross-functionally with Product, Engineering, Account Management and Customer teams to ensure successful outcomes

• Build Production-Grade Systems • Design and implement scalable Python services powering AI-driven workflows • Develop resilient, observable systems with strong error handling and compliance traceability • Work within a serverless, event-driven AWS architecture • Own Delivery End-to-End • Take ownership of features from design through to production • Collaborate with Product, AI Platform, and Compliance teams • Contribute to sprint planning, estimation, and delivery • Shape AI & Agentic Systems • Build intelligent workflows using LLMs and agentic architectures • Integrate AI models into production systems • Design guardrails, policy enforcement, and behavioural monitoring for AI agents • Mentor & Elevate the Team • Support and guide junior and mid-level engineers • Lead code reviews and promote engineering best practices • Contribute to documentation, demos, and knowledge sharing • Drive AI-Augmented Engineering • Use AI coding tools to accelerate development and improve quality • Help define best practices for AI-assisted software engineering

• Build and evolve a Compliance Knowledge Hub, translating regulation into product-ready logic • Act as a subject matter expert across UK and European regulatory frameworks • Work closely with product, engineering and AI teams to embed compliance into features • Support development of AI/LLM capabilities with real-world compliance insight • Partner with commercial teams to shape product direction and growth • Engage clients and industry stakeholders to refine and enhance solutions • Monitor regulatory changes and translate them into actionable product requirements • Support client risk and compliance transformation initiatives • Contribute to strategic direction, including Aveni’s Risk 2030 vision

• Own performance, development, and progression of your team • Own the individual performance and development of your direct reports, setting a high bar, giving honest feedback, and creating conditions for engineers to grow into real ownership. • Build a self-organised team, initially playing a facilitating role in ceremonies, with the explicit goal of no longer being needed to run the process. • Partner with Product on roadmap planning, making sure engineers understand the context behind the work, not just the tickets in front of them. • Own your team's delivery, keeping engineers unblocked, priorities clear, and commitments met. • Spot patterns in how your team operates, design better ways of working, and build the automation that powers those improvements. • Stay close enough to the codebase to spot friction, improve developer tooling, and engage credibly in technical discussions, without stepping onto the critical path. • Drive hiring alongside our Recruitment team, contributing to interview process design, and a high-quality candidate experience. • Set the cultural expectation around AI coding tools, building team-wide norms for responsible, high-quality AI-assisted engineering.

• Translate product initiatives into real-world workflows: Turn high-level product ideas into detailed advice processes, requirements and logic. • Act as a financial advice SME: Provide expertise on advice processes, suitability standards and FCA expectations. • Support solution design: Work with engineering teams to ensure features reflect real adviser workflows and operational realities. • Validate product outputs: Review and test outputs from an adviser, paraplanner and compliance perspective. • Drive product improvement: Identify gaps, edge cases and opportunities to refine solutions during development.

• Translate product initiatives into real-world workflows: Turn high-level product ideas into detailed advice processes, requirements and logic. • Act as a financial advice SME: Provide expertise on advice processes, suitability standards and FCA expectations. • Support solution design: Work with engineering teams to ensure features reflect real adviser workflows and operational realities. • Validate product outputs: Review and test outputs from an adviser, paraplanner and compliance perspective. • Drive product improvement: Identify gaps, edge cases and opportunities to refine solutions during development.

• Setting technical direction for your squad. • Owning architectural decisions, managing technical risk, and representing the engineering perspective at our Architecture Review Board. • Leading AI-powered product development: designing LLM workflows, RAG pipelines, and agentic features that run on top of our AI platform in production, at scale. • Driving adoption of AI coding tools across the full SDLC, from planning and design through code review, testing, deployment and setting the bar for how the squad works. • Delivering production grade code across the full stack: Node.js or Python backends, event-driven AWS architecture, and React where the squad needs it. • Mentoring and growing engineers across AI and full-stack domains, through code reviews, design documents, and day-to-day pairing. • Collaborating with platform teams to consume shared capabilities and contribute reusable patterns back, not building one-off solutions when a platform component will do. • Ensuring everything we ship is compliant-by-design, auditable, and built to the standards expected of software in FCA regulated financial services.

• Setting technical direction for your squad • Owning architectural decisions, managing technical risk, representing engineering perspective at Architecture Review Board • Leading AI-powered product development: designing LLM workflows, RAG pipelines, agentic features on AI platform • Driving adoption of AI coding tools across the full SDLC • Delivering production grade code across the full stack: Node.js or Python backends, event-driven AWS architecture, and React • Mentoring and growing engineers across AI and full-stack domains • Collaborating with platform teams to consume shared capabilities and contribute reusable patterns back • Ensuring everything shipped is compliant-by-design, auditable and built to standards expected in FCA regulated financial services

• Building and scaling products that sit on top of FinLLM (the UK's first LLM for the Financial Services industry) and other LLMs. • Designing and implementing robust event-driven microservices and APIs. • Building scalable, cloud-native systems on AWS. • Developing modern, responsive React front-ends. • Integrating LLM-powered capabilities into real-world workflows. • Collaborating closely with product and AI specialists to turn ideas into reliable, customer-ready features.

• Partner with the Sales team during pre-sales engagements, pilots and trials to support successful client onboarding. • Configure and customise Aveni’s platform to meet client-specific advice documentation and compliance requirements. • Build and refine AI-driven document templates aligned to firms’ suitability report structures and internal standards. • Support onboarding and training for Aveni Assist pilot users. • Work with clients to understand their quality assurance frameworks and advice review processes. • Define and configure QA questions, scoring criteria, evidence requirements and edge cases within the platform. • Gather and analyse pilot metrics, usage data and insights for internal reviews and client presentations. • Support the Sales leadership team with pilot reporting and outcomes. • Provide structured feedback to Product and Engineering teams based on real-world client usage.