Principal AI Engineering Lead in London

Responsibilities

• 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.
• 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 scalability.
• Build 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; 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

• Experience fine‑tuning models or building advanced AI algorithms.
• Familiarity with Docker, Kubernetes, orchestration tools and workflow tools such as Airflow or Kubeflow.
• Background in investment management, capital markets, or asset servicing – particularly familiarity with trading platforms, quantitative research tooling, or data pipelines subject to financial regulation.
• Hands‑on experience architecting or operating retrieval‑augmented generation systems, agent‑orchestration layers, model‑evaluation harnesses, or LLM‑backed product features at production scale.

Supervisory Responsibilities

• Directly supervise a small team of AI engineers and enablement specialists, setting objectives, conducting performance reviews, and supporting their professional growth.
• Provide day‑to‑day technical direction on delivery engagements, including code‑review standards, architectural decisions, and prioritisation of the team's project backlog.

Potential for Growth

• Leadership development programs.
• Regular training.
• Career development services.
• Continuing education courses.
• Certification pathways across major AI, cloud, and developer‑tooling platforms.
• Direct visibility with executive leadership through a high‑profile transformation initiative.
• Scope to build a new function from scratch – this role is being created for the first time, with the mandate to define how AI engineering capability scales across the firm and evolves into AI‑native product delivery.

Benefits

• Position may be eligible to receive an annual discretionary bonus award from the profit pool.
• Competitive compensation, pension/retirement plans, and a comprehensive range of health, wellbeing, and lifestyle benefits.

Equal Opportunity Employer

Janus Henderson Investors is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status. All applications are subject to background checks.