Senior AI Engineer

Janus Henderson Investors•Denver, CO

16d•$130,000 - $220,000•Hybrid

About The Position

Janus Henderson is undergoing a company-wide AI transformation to become the most technologically advanced asset manager in the industry. The AI capability is centralized under the Head of AI, with AI Technology responsible for building, governing, and operating the software. AI Engineering is the core product and platform engineering team within this function. Unlike Forward Deployed Engineering, which embeds with business units to solve specific problems, AI Engineering develops enterprise products that all other teams rely on. These include Nexus, an agentic workspace for building, testing, running, and managing governed AI applications, agents, and shared skills; Accio, a centralized MCP server acting as a gateway and logic center for enterprise datasets; and the orchestration, evaluation, and observability services supporting projects like Libros and PRISM delivered with Percepta. As a Senior Applied AI Engineer, reporting to the Principal AI Engineer, you will be responsible for leading the delivery of significant parts of this AI estate. This involves designing and building agentic applications and platform services, guiding them through evaluation and AI governance checkpoints into production, and then owning them post-launch. You will collaborate with AI Architecture to establish implementation patterns, review the work of other engineers, and serve as the go-to person for resolving unexpected production system behaviors. Additionally, you will work closely with Percepta's engineers to ensure platforms are transferred with understandable designs and multiple team members capable of extending them. You will also be responsible for the end-to-end development of business applications for specific areas of the firm. This includes not only the shared horizontal products but also targeted applications built on the AI stack for named business areas, from initial requirements through to supported production services. Distribution is the initial focus, with a significant effort dedicated to decommissioning existing SaaS solutions by building capabilities in-house. You will own these builds end-to-end, leading to the retirement of displaced products and consolidating the firm's tools onto governed, in-house capabilities. This approach will extend to other business areas such as trading, investment risk, client servicing, and operations, prioritizing builds where in-house ownership offers greater control, cost savings, or unique capabilities not available from vendors. Your technical toolkit includes Python and SQL, LLMs and agent frameworks, MCP, Azure AI Foundry via the AI Team’s model gateway, Snowflake and Microsoft Fabric, and Azure with Terraform, Docker, and CI/CD. As AI Engineering is being established for the first time, you will have the opportunity to shape the team's working methods rather than inheriting established routines.

Requirements

At least six years in software, data, or platform engineering, with a track record of shipping and operating production systems rather than prototypes.
Production experience with LLM applications and agentic systems, covering prompt and context engineering, agent development, retrieval, tool use, evaluation, and deployment at scale.
Strong Python and SQL, with the judgement to write code others can maintain and extend.
Hands-on experience building agentic capability such as MCP servers, tools, skills, or connectors for other systems and agents to consume.
Hands-on experience with a major cloud, ideally Azure, including containers or serverless compute, infrastructure as code, CI/CD, identity, RBAC, and secret management.
Experience owning services in production — monitoring, incident investigation, upgrades, lifecycle management — and establishing evaluation and observability for AI systems.
Good judgement in a regulated environment, translating security, privacy, risk, and audit requirements into working technical controls.
Experience mentoring engineers and leading technical work without relying on reporting authority, and clear communication with engineers, control functions, and business stakeholders.

Nice To Haves

Asset management or financial services domain knowledge, particularly the investment process, distribution, or front-office workflows.
Azure AI Foundry, Azure OpenAI, Anthropic Claude, model gateways, inference routing, or agent orchestration platforms.
Snowflake, Microsoft Fabric / OneLake, or comparable governed enterprise data platforms.
TypeScript or a second production language, and front-end experience for user-facing AI applications.
Experience building internal developer platforms or golden-path patterns used by other engineering teams, or taking partner-built software into internal ownership.

Responsibilities

Design, build, and productionize AI products and shared platform services, taking a workstream end to end and owning it through to live running.
Build for reuse, turning capability proven in one product into shared components, libraries, skills, tools, MCP servers, and connectors.
Implement infrastructure as code, own CI/CD pipelines, and accept services into operation only when ownership, controls, and support are clear.
Investigate incidents and defects in the services you own, lead recovery, carry fixes through to the underlying cause, and act as L3 escalation.
Build agentic applications and workflows covering agent design, prompt and context engineering, tool use, memory, retrieval, and human-in-the-loop controls.
Implement model selection, routing, and fallback through the model gateway, handling provider change without hiding differences in capability, cost, or behaviour.
Build the orchestration layer that long-running and multi-step agents depend on, covering permissions, workload isolation, safe execution, and the applications built on it.
Test the claims made for new models, frameworks, and patterns before recommending we adopt them.
Build governed, AI-ready views, indexes, semantic context, and connectors over Snowflake, enterprise platforms, APIs, and external providers.
Extend Accio so new datasets and downstream MCP servers are reachable through one governed interface rather than one-off integrations.
Build ingestion pipelines and data models where source data does not arrive usable, while canonical source ownership stays with the relevant Technology team.
Own lineage, quality, and freshness for the data your products depend on, and implement least-privilege access for users, agents, tools, and service identities.
Own the end-to-end build of targeted business applications on the AI stack — Distribution first, then areas such as trading, investment risk, client servicing, and operations — delivered for a named business owner rather than for every team at once.
Drive SaaS decommissioning: where a bought subscription can be replaced by an in-house build, own the delivery of that replacement end-to-end, see the displaced product retired, and work with Procurement and Finance to consolidate the licence.
Prioritise the builds where in-house ownership gives the firm more control, lower cost, or capability no vendor sells, and reuse existing Accio datasets, skills, and connectors instead of rebuilding them per application.
Take each application through the same evaluation, controls, and release evidence as the shared products, and own its support and lifecycle once live or hand it to a clear owner.
Build evaluation and regression suites for models, prompts, agents, and platform changes, meet the agreed thresholds before release, and instrument what you build for quality, safety, reliability, latency, drift, usage, and cost.
Implement the controls defined by AI Governance Implementation and AI Security as code and secure defaults, and produce release evidence proportionate to the risk of the use case, generated by the platform rather than assembled after the event.
Work alongside Percepta engineers on Libros, PRISM, and the wider estate, holding joint work to our engineering standards and raising maintainability problems while they are still cheap to fix.
Mentor AI Engineers through pairing, design discussion, and code review, and help establish the team’s engineering standards, agentic SDLC, and definition of done.
Work with AI Architecture to turn reference patterns into implementations teams use, partner with Forward Deployed Engineering so proven solutions become supported shared capability, and feed adoption data from the AI Enablement Partners into the backlog.

Benefits

Hybrid working and reasonable accommodations
Generous Holiday policies
Excellent Health and Wellbeing benefits including corporate membership to Wellhub
Paid volunteer time to step away from your desk and into the community
Support to grow through professional development courses, tuition/qualification reimbursement and more
Maternal/paternal leave benefits and family services
Unique employee events and programs including a 14er challenge
Complimentary beverages, snacks and all employee Happy Hours
Competitive compensation
Pension/retirement plans
Various health, wellbeing and lifestyle benefits
Annual discretionary bonus award from the profit pool