Senior AI Engineer

DataJobs; Denver (CO); On-site; USD 130,000 - 220,000; Full time

Benefits offered by this job

Hybrid working

Wellbeing benefits

Tuition reimbursement

Volunteer time

Parental leave

Employee events

Job summary

Janus Henderson is seeking a Senior Applied AI Engineer in Denver to lead major parts of the firm's AI transformation. You will design, build, evaluate, and productionise agentic applications and platform services, owning them through live operation with governed data access and model evaluation.

The role reports to the Principal AI Engineer and sits within AI Technology, supporting Nexus, Accio, Libros, PRISM, and Percepta integrations while mentoring others and driving secure, scalable AI

Qualifications

• 6+ years in software, data, or platform engineering with production systems.
• Production experience with LLM applications and agentic systems, including prompt/context engineering and deployment at scale.
• Strong Python and SQL with maintainable code.
• Hands-on experience building agentic capability (MCP servers, tools, skills, connectors).
• Experience with a major cloud (ideally Azure) including containers/serverless, IaC, CI/CD, identity, RBAC, secrets management.
• Experience owning services in production including monitoring, incidents, upgrades, lifecycle management.
• Ability to translate security, privacy, risk, and audit requirements into technical controls.

Responsibilities

• Design, build, and productionise AI products and shared platform services.
• Make reusable components, libraries, and connectors for other teams.
• Implement infrastructure as code, own CI/CD pipelines, ensure operational readiness.
• Investigate incidents and defects, lead recovery, and perform root-cause analysis.
• Build agentic applications and workflows including prompt/context engineering and tool use.
• Implement model routing, fallback, and governance across providers.
• Build orchestration layers for long-running agents with proper permissions and isolation.
• Test models and patterns before adoption, ensure release evidence.

Skills

Python

SQL

LLMs

Agent frameworks

MCP

Tools

Azure AI Foundry

Snowflake

Microsoft Fabric

Azure

Terraform

Docker

CI/CD

Job description

Janus Henderson is hiring a Senior Applied AI Engineer to lead delivery of major parts of the firm-wide AI transformation. The role designs, builds, evaluates, and productionises agentic applications and platform services, then owns them through live running, including governed data access and model evaluation.

Role Overview

This is a senior applied AI engineering position reporting to the Principal AI Engineer. AI capability is centralized under the Head of AI, and AI Technology builds, governs, and runs the software. AI Engineering serves as the core product and platform engineering team, supporting enterprise agentic products including Nexus (agentic workspace) and Accio (centralised MCP server), alongside orchestration, evaluation, and observability services beneath Libros and PRISM, delivered with Percepta.

Responsibilities

• Design, build, and productionise AI products and shared platform services, taking a workstream end to end and owning it through live running.
• Build for reuse by turning proven capability 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 services you own, lead recovery, carry fixes through to underlying cause, and act as L3 escalation.
• Build agentic applications and workflows across 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 while handling provider change without hiding differences in capability, cost, or behaviour.
• Build orchestration layers for long-running, multi-step agents, covering permissions, workload isolation, safe execution, and dependent applications.
• Test claims for new models, frameworks, and patterns before recommending adoption.
• 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 a single governed interface rather than one-off integrations.
• Build ingestion pipelines and data models when source data is not usable, while keeping canonical source ownership with the relevant Technology team.
• Own lineage, quality, and freshness for data that 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, with initial focus on Distribution and expansion to trading, investment risk, client servicing, and operations.
• Drive SaaS decommissioning for named business areas by replacing bought subscriptions with in-house builds, owning end-to-end delivery, retiring displaced products, and coordinating with Procurement and Finance to consolidate licences.
• Prioritise builds where in-house ownership improves control, reduces cost, or delivers capability not available from vendors, reusing existing Accio datasets, skills, and connectors.
• Take applications through the same evaluation, controls, and release evidence as shared products, and own support and lifecycle once live or hand over to a clear owner.
• Build evaluation and regression suites for models, prompts, agents, and platform changes, meeting agreed thresholds and instrumenting quality, safety, reliability, latency, drift, usage, and cost.
• Implement AI Governance Implementation and AI Security controls as code with secure defaults, producing release evidence proportionate to risk generated by the platform.
• Work with Percepta engineers on Libros, PRISM, and the wider estate, ensuring engineering standards and surfacing maintainability issues while they are inexpensive to resolve.
• Mentor AI Engineers through pairing, design discussion, and code review, helping establish engineering standards, agentic SDLC, and definition of done.
• Partner with AI Architecture to turn reference patterns into implementations used by teams, work with Forward Deployed Engineering so proven solutions become supported shared capability, and feed adoption data into the backlog.

Requirements

• At least 6 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, including prompt and context engineering, agent development, retrieval, tool use, evaluation, and deployment at scale.
• Strong Python and SQL, with 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, including monitoring, incident investigation, upgrades, lifecycle management, and establishing evaluation and observability for AI systems.
• Good judgement in a regulated environment by translating security, privacy, risk, and audit requirements into working technical controls.
• Experience mentoring engineers and leading technical work without relying on reporting authority, plus clear communication with engineers, control functions, and business stakeholders.

Technologies

• Python, SQL, LLMs, agent frameworks, MCP
• Azure AI Foundry, Snowflake, Microsoft Fabric, Azure
• Terraform, Docker, CI/CD

Nice to Have

• 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, or taking partner-built software into internal ownership.

Supervisory Responsibilities

• No line-management responsibility.
• Leads technical delivery, sets implementation standards, and mentors AI Engineers.
• May act as technical lead for a product or platform workstream.

Compensation

• Base salary range: USD 130,000 - 220,000 per year.
• Range is estimated for the role; actual pay may differ.
• This position will be open through [add date].
• Colorado law requires an estimated closing date for job postings.

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 and tuition/qualification reimbursement.
• Maternal/paternal leave benefits and family services.
• Unique employee events and programs including a 14er challenge.
• Complimentary beverages, snacks and all employee Happy Hours.
• Position may be eligible for an annual discretionary bonus award from the profit pool.
• Comprehensive total rewards package including competitive compensation, pension/retirement plans, and various health, wellbeing and lifestyle benefits.

Location

Denver, CO (onsite)

Minimum Experience

6+ years of relevant experience.

Employer Context

Janus Henderson is undertaking a firm-wide AI transformation to become the most technologically sophisticated asset manager in the industry. The AI Engineering team builds and governs enterprise products that other teams depend on, including Nexus, Accio, and services beneath Libros and PRISM delivered with Percepta. As Senior Applied AI Engineer, you will lead delivery of major parts of that estate by moving solutions from evaluation and AI governance checkpoints into production ownership.