Principal AI Engineering Lead

About the role

Why work for us? A career at Janus Henderson is more than a job, it's about investing in a brighter future together. Our Mission at Janus Henderson is to help clients define and achieve superior financial outcomes through differentiated insights, disciplined investments, and world-class service. We will do this by protecting and growing our core business, amplifying our strengths and diversifying where we have the right. Our Values are key to driving our success, and are at the heart of everything we do: Clients Come First - Always | Execution Supersedes Intention | Together We Win | Diversity Improves Results | Truth Builds Trust If our mission, values, and purpose align with your own, we would love to hear from you! Your opportunity Are you passionate about pushing the boundaries of AI and emerging technologies? Do you thrive on transforming complex data into real-time, actionable insights? Are you looking to apply deep technical expertise to solve high-impact business challenges at scale? Do you want to lead high-performing engineering teams building next-generation AI-driven platforms? Are you energised by partnering with senior business stakeholders to shape strategy and deliver measurable outcomes? What you will do Own End-to-End AI Engineering Delivery: Design, build, and deploy production-grade AI/ML systems (LLMs, agents, predictive models) across the full lifecycle, including data ingestion, model integration, evaluation, and ensuring production readiness within a regulated environment Develop Reference Architectures & Accelerators: Create reusable frameworks, SDKs, and reference implementations (e.g., agent orchestration patterns, prompt frameworks, RAG pipelines) to standardise AI development across engineering teams Hands-on Engineering Leadership: Contribute directly to codebases (Python, APIs, orchestration layers), perform code reviews, and enforce engineering standards across AI, data, and application layers Implement AI-Native Development Patterns: Drive adoption of advanced engineering practices including LLM-based development workflows, autonomous agents, retrieval-augmented generation (RAG), and AI-augmented CI/CD pipelines Define AI Platform & Tooling Strategy: Architect and influence enterprise AI platforms, including model integration layers, vector databases, orchestration frameworks, and developer tooling (e.g., Copilot, prompt management, evaluation pipelines) Engineer Scalable Data & Model Pipelines: Design and optimise real-time and batch data pipelines for AI workloads, ensuring performance, observability, and scalability across cloud-native environments Operationalise AI Systems (MLOps / LLMOps): Establish robust deployment, monitoring, and evaluation pipelines (model performance, drift detection, prompt/version management, A/B testing) Embed Security, Governance & Responsible AI: Implement guardrails including access controls, audit logging, model validation, data lineage, and compliance with regulatory and responsible AI requirements Assess Technical Maturity & Remove Bottlenecks: Conduct deep-dive assessments of engineering workflows, tooling, and architecture to identify constraints and optimise developer productivity and delivery velocity Define Engineering Metrics & Telemetry: Instrument platforms to track system performance and developer productivity metrics (latency, throughput, error rates, cycle time, deployment frequency) Enable Distributed Engineering Adoption: Build and scale internal capability through code-first enablement, technical playbooks, and deep-dive workshops focused on real-world implementations Drive Cross-Team Technical Integration: Align AI engineering patterns across platform, data, and application teams to ensure interoperability, consistency, and reuse Track Emerging AI Technologies: Evaluate and integrate advancements in LLMs, agent frameworks, orchestration protocols, and developer tooling into production-ready enterprise patterns Produce Engineering Artefacts: Maintain architecture blueprints, ADRs, API contracts, runbooks, and reusable code assets to ensure maintainability and scalabilityBuild Enterprise AI Capability: Design and deliver a structured capability uplift programme across engineering, data, architecture, and product disciplines, with role-specific learning pathways Required skills Ability to design scalable AI systems integrated into products and enterprise platforms Experience applying analytics and statistical techniques to drive AI performance Experience deploying AI solutions on cloud platforms Experience building LLM-powered applications, Retrieval-Augmented Generation (RAG) systems and Agent-based workflows and orchestration patterns Ability to Lead AI initiatives and define technical direction and Mentor engineers and conduct code/architecture reviews Proven track record of delivering AI solutions from idea to production Strong communication skills to explain complex technical concepts to stakeholders Nice to have Experien

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