Senior Applied AI Engineer – Enterprise Platform Lead

Janus Henderson Group; Denver (CO); Hybrid; USD 130,000 - 220,000; Full time

Benefits offered by this job

Health and Wellbeing benefits

Paid volunteer time

Professional development support

Maternal/paternal leave

Job summary

Janus Henderson Group is seeking a Senior Applied AI Engineer in Denver to lead major parts of our AI estate—designing and building agentic applications and platform services, taking them through evaluation and governance into production.

You will set implementation patterns, mentor engineers, and own end-to-end business applications on the AI stack—driving distribution-focused builds, SaaS decommissioning, and cross-team collaboration with Percepta using Python and SQL.

Qualifications

• At least six years in software, data, or platform engineering.
• Production experience with LLM applications and agentic systems.
• Strong Python and SQL coding skills.
• Hands-on experience building MCP servers, tools, or connectors.
• Experience owning services in production including monitoring and incident response.
• Good judgement in a regulated environment and risk controls.
• Experience mentoring engineers and leading technical work.

Responsibilities

• Build and run AI products and shared platform.
• Design, build, and productionise 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.
• Engineer agents, orchestration, and the application layer.
• 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.
• Make enterprise data AI-ready
• 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.
• Build business-area applications and decommission SaaS
• 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 in evaluation and control, and work with the team
• 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.

Skills

Six years+ software/data/platform

LLM prod experience

Python & SQL

MCP servers

Agentic systems

Regulatory compliance

Mentoring

Job description

Janus Henderson Group is seeking a Senior Applied AI Engineer in Denver to lead major parts of our AI estate—designing and building agentic applications and platform services, taking them through evaluation and governance into production.

You will set implementation patterns, mentor engineers, and own end-to-end business applications on the AI stack—driving distribution-focused builds, SaaS decommissioning, and cross-team collaboration with Percepta using Python and SQL.