AI Engineer

Overview

Why work for us?

Janus Henderson offers a career that contributes to a brighter future for clients and communities through differentiated insights, disciplined investments, and world-class service. Our mission is to protect and grow our core business, amplify our strengths, and diversify where we have the right. Our values guide 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 yours, we would love to hear from you!

• Design, build, and deploy AI applications using RAG and agentic frameworks (Lang Chain, Microsoft Agent Framework), including data ingestion, embeddings, vector indexing, retrieval, and UI development (e.g., Streamlit).
• Deliver production-grade AI systems on Azure Cloud, applying the Azure Well-Architected Framework to ensure scalability, reliability, security, and cost efficiency.
• Develop and maintain model pipelines using Databricks and Azure services, and build AI agents and intelligent workflows leveraging the Snowflake Cortex suite.
• Write clean, maintainable Python code using test-driven development (TDD), CI/CD workflows, automated testing, and modern engineering best practices.
• Implement AI observability and evaluation, including monitoring, logging, performance metrics, guardrails, and drift detection for LLM-based applications.
• Apply NLP and LLM techniques to extract insights from unstructured data and deliver business-ready outputs.
• Partner with business stakeholders to gather requirements, identify AI opportunities, and deliver well-tested, end-to-end solutions with a strong sense of ownership.
• Collaborate across technology, data, and risk teams to integrate AI solutions into the enterprise environment and ensure adherence to governance standards.
• Support a culture of innovation and responsible AI, staying current on emerging RAG patterns, agentic capabilities, and Azure AI toolsets.
• Carry out other duties as assigned.

• Hybrid working and reasonable accommodations
• Generous Holiday policies
• 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
• Complimentary subscription to Headspace – the mindfulness app
• Corporate membership to Class Pass and other health and well-being benefits
• Unique employee events and programs including a 14er challenge
• Complimentary beverages, snacks and all employee Happy Hours

• Master’s degree in Computer Science, Software Engineering, or related technical field (or equivalent practical experience).
• Minimum 3 years of experience in AI or software engineering, building and deploying production-grade applications in a cloud environment.
• Hands-on experience designing and delivering Generative AI applications, with at least 1+ year focused on RAG-based systems (embeddings, vector stores, retrieval strategies, evaluation pipelines).
• Experience building agentic AI systems (e.g., Lang Chain Agents, Microsoft Agent Framework, Snowflake Cortex Agents).
• Strong Python engineering skills, including building modular applications, APIs, and automation frameworks.
• Experience with test-driven development (TDD), CI/CD pipelines, automated testing, and modern SDLC best practices.
• Deep understanding of Azure Cloud services and solution architecture principles, including the Azure Well-Architected Framework.
• Experience implementing LLM evaluation, monitoring, and observability (telemetry, drift, guardrails, performance tracking).
• Strong understanding of NLP, LLM behaviour, and prompt engineering best practices.
• Ability to engage stakeholders, translate requirements into technical solutions, and deliver high-quality, production-ready AI applications with a strong sense of ownership.

• Experience building front-end interfaces for AI apps (e.g., Streamlit, Dash, or similar).
• Experience building and maintaining data/model pipelines using Databricks, Azure Data services, and Snowflake, including…