Who are we?

Howden is a global insurance group with employee ownership at its heart. Together, we have pushed the boundaries of insurance. We are united by a shared passion and no-limits mindset, and our strength lies in our ability to collaborate as a powerful international team comprised of 24,000 employees spanning over 56 countries.

People join Howden for many different reasons, but they stay for the same one: our culture. It’s what sets us apart, and the reason our employees have been turning down headhunters for years. Whatever your priorities – work / life balance, career progression, sustainability, volunteering – you’ll find like-minded people driving change at Howden.

Role

Senior AI Engineer 

Location: United States (Remote)

Reports to: AI Lead

Employment Type: Full-time, Exempt

Direct Reports: None 

Who are we?

Howden is a collective, a group of talented and passionate people all around the world. Together, we have pushed the boundaries of insurance. We are united by a shared passion and no-limits mindset, and our strength lies in our ability to collaborate as a powerful international team comprised of 20,000 employees spanning over 100 countries.

Our people are our biggest asset as well as our largest shareholder group and are everything that makes us unique; our inclusive culture, the quality service we offer our clients, and our continued growth, all come from our people-first approach. There's no such thing as individual success. We all need to play our part, contributing our skills and experience to make a true difference. That's Howden.

Why work at Howden?

We have always been employee-owned and driven by entrepreneurial spirit. Right from the beginning, we've focused on employing talented individuals and empowering them to make a difference for clients and the company, whilst building successful and fulfilling careers at the same time. Simply put, we hire talented specialists and give them what they need to make a difference.

Always have, always will.

People join Howden for many different reasons, but they stay for the same one: our culture. It's what sets us apart, and the reason our employees have been turning down disappointed head-hunters for years. Whatever your priorities, work/life balance, career progression, sustainability, volunteering, you'll find like-minded people driving change at Howden.

What is the role?

Senior AI Engineer builds and ships AI enabled software. This is a hands-on build role on Howden's US AI Engineering team: writing the agents, the pipelines, the integrations, and the infrastructure as code that puts AI capability into production on Azure. This is a software engineering role first. The work is production code in Python, Node.js and TypeScript, and .NET (C#), sitting on top of Azure services and Howden data.

You will work from solution designs and integration patterns set by the AI Lead and enterprise architecture, and you will own the implementation end to end. Expect to spend most of your time in code, in Terraform, and in CI/CD pipelines, with a heavy emphasis on agent-assisted development: using coding agents and AI development tooling to move faster than a conventional engineering pace and knowing when to trust the output and when to rewrite it.

This role is self-directed. You will be handed an outcome and a rough shape, and you are expected to break it down, sequence it, unblock yourself, and deliver production-quality work collaborating with other team members. 

What success looks like

• AI agents and services running in production on Azure, built by you, with tests, telemetry, and deployment automation attached.

• Terraform and pipeline code that provisions and deploys AI workloads repeatably across environments with no manual steps.

• Vendor AI platforms wired into Howden systems through working integrations, not slide decks.

• Knowledge bases and retrieval layers that agents can rely on, with retrieval quality measured against real queries rather than assumed.

• Agent-assisted development practices are used daily and shared with the team as working examples: prompts, harnesses, evaluation scripts, and repository conventions that make coding agents productive on our codebase.

• Work delivered from a stated outcome with minimal direction, escalating early when something genuinely needs a decision above your level.

• Design input that changes outcomes: options, trade-offs, and working spikes brought to the AI Lead and architecture before decisions are locked.

What will you be doing?

AI Agent & Application Development

• Build production AI agents and conversational solutions on Azure using Azure OpenAI Service, Azure AI Foundry, Azure Bot Framework, and agent orchestration frameworks.

• Implement multi-agent orchestration, tool and function calling, retrieval-augmented generation, and context and memory management in code.

• Build retrieval pipelines and knowledge bases using Azure AI Search and vector stores, including chunking strategies, embedding pipelines, and document processing.

• Write evaluation harnesses and regression tests for AI behavior: golden datasets, scoring scripts, and automated checks that run in CI before anything ships.

• Develop RESTful and event-driven APIs and services that expose AI capability to internal applications, using Azure Functions, Container Apps, API Management, and Service Bus.

• Refactor and harden prototypes into supportable production code with error handling, retries, rate limiting, and cost controls.

Data & Knowledge Engineering

• Build and maintain the knowledge layer behind our AI systems: source profiling, extraction, cleansing, normalization, and enrichment across structured and unstructured data.

• Model the domain. Define taxonomies, ontologies, entity and relationship models, and metadata schemas that give agents a consistent view of insurance data such as clients, policies, carriers, submissions, and claims.

• Design and tune retrieval quality end to end chunking strategy, embedding choice, hybrid and semantic search, metadata filtering, reranking, and groundedness evaluation.

• Build ingestion and refresh pipelines that keep knowledge bases current, with lineage, versioning, change detection, and reconciliation against source systems.

• Analyze system and usage data to find where AI is failing query logs, retrieval hit rates, groundedness scores, cost per interaction, and user feedback. Act on what the data shows.

• Write the SQL, transformations, and analysis needed to answer your own data questions rather than waiting on another team.

Agent-Assisted Development

• Use coding agents and AI development tooling (Claude Code, GitHub Copilot, and similar) as a primary part of your daily workflow for implementation, refactoring, test generation, and debugging.

• Build and maintain the scaffolding that makes agent-assisted development work on our repositories: context files, tool definitions, repository conventions, task decomposition patterns, and reusable prompt assets.

• Apply engineering judgment to agent output. Review generated code as rigorously as human-written code and know where the tooling saves hours and where it creates clean up work.

• Automate repetitive engineering work with scripted agents: migrations, test backfill, documentation generation, dependency upgrades, and integration scaffolding.

• Share working patterns with the team through examples in the codebase rather than through process documents.

Infrastructure as Code & Deployment Automation

• Write and maintain Terraform for Azure AI workloads: Azure OpenAI deployments, AI Search, Cosmos DB, Storage, networking, Key Vault, managed identities, and role assignments.

• Own module structure, state management, workspace and environment separation, variable and secret handling, and drift detection for the infrastructure you build.

• Build CI/CD pipelines in Azure DevOps or GitHub Actions covering plan and apply gates, automated testing, container builds, environment promotion, and rollback.

• Containerize AI services and deploy Azure Container Apps, Container Instances, or AKS with sensible scaling and resource configuration.

• Keep environments reproducible. Anything created by hand in the portal gets replaced by code.

Vendor Platform & API Integration

• Implement integrations between vendor AI platforms and Howden systems using vendor APIs, SDKs, webhooks, and connectors.

• Handle authentication and authorization with Entra ID, OAuth 2.0, managed identities, and API keys, with secrets managed through Key Vault.

• Build and test data flows between third-party platforms and internal systems, including error handling, replay, and reconciliation.

• Support technical evaluation and proof-of-concept work on vendor platforms by building working spikes against real APIs.

• Ship AI capability into Microsoft Teams, web channels, and other surfaces where our users already work.

Observability & Production Support

• Instrument AI services with Azure Monitor, Application Insights, and Log Analytics, including distributed tracing, token and cost telemetry, and structured logging.

• Build dashboards and alerts for latency, error rates, throughput, model usage, and cost.

• Participate in production support for AI systems: triage, root cause analysis, fixes, and post-incident follow-through.

• Write runbooks and troubleshooting notes for the systems you build so others can operate them.

Responsible AI in Practice

• Implement content filtering, safety guardrails, and Azure AI Content Safety configurations in the solutions you build.

• Implement PII detection and redaction in AI interactions and data pipelines.

• Build enterprise security and data privacy standards: access controls, encryption, network isolation, and audit logging.

Solution Design Contribution

• Contribute to solution designs alongside the AI Lead and enterprise architecture: bring options, trade-offs, cost and latency implications, and a recommendation grounded in what you have built.

• Build spikes and reference implementations that prove or kill a design approach before the team commits to it.

• Break approved designs into technical work: component boundaries, data contracts, interfaces, sequencing, and realistic estimates.

• Review vendor and internal integration designs and flag gaps in security, scale, cost, or supportability early.

• Document the as-built design: what shipped, where it deviated from the original approach, and why.

Collaboration

• Work inside an Agile team with product managers, engineers, architecture, and security to plan, refine, and deliver iteratively.

• Review peer pull requests and raise the quality bar through code review rather than through process.

• Flag design gaps and integration risks early to the AI Lead and enterprise architecture.

• Write clear technical documentation for the code and infrastructure you own.

What are we looking for?

Minimum Qualifications

• Bachelor's degree in computer science, Engineering, Information Systems, or a related technical field, or equivalent hands-on experience.

• 5+ years of professional software engineering experience, including 2+ years of building and shipping AI or machine learning solutions to production.

• Strong hands-on coding background with production experience in Python, plus working proficiency in Node.js or TypeScript and .NET (C#). Depth in one and real working ability in the others.

• Solid software engineering fundamentals: Git workflow, automated testing, code review, debugging, performance profiling, and writing code other engineers can maintain. Expect to walk through the code you have written.

• Hands-on experience building applications with large language models: prompt engineering, retrieval-augmented generation, function and tool calling, and agent frameworks.

• Production experience with Azure AI services such as Azure OpenAI Service, Azure AI Foundry, Azure AI Search, or Azure Machine Learning.

• Hands-on Terraform experience provisioning cloud infrastructure, including modules, state management, and multi-environment workflows.

• Experience building and maintaining CI/CD pipelines in Azure DevOps, GitHub Actions, or GitLab.

• Working experience with containers and cloud-native deployment patterns: Docker, serverless functions, and managed container services.

• Experience integrating third-party APIs and SDKs into enterprise systems, including authentication and secure connectivity.

• Data and analytics background: strong SQL, data modeling, and hands-on work with both structured and unstructured sources, including profiling and cleaning messy enterprise data.

• Experience building or curating knowledge bases for search or AI retrieval, including taxonomies, ontologies, entity and metadata models, and content quality management.

• Experience instrumenting and supporting production systems with Azure Monitor, Application Insights, or comparable observability tooling.

• Practical daily use of AI coding assistants or coding agents in a professional development workflow.

• Demonstrated ability to take an outcome and deliver it independently, breaking down the work and resolving blockers without close direction.

• Experience contributing to solution designs and technical trade-off discussions, presenting options and recommendations to architects or engineering leadership.

• Clear written and verbal communication with technical and non-technical colleagues.

Preferred Qualifications

• Microsoft Azure certifications (AI Engineer Associate, Azure Developer Associate, or Solutions Architect) or comparable cloud credentials.

• Experience with agent orchestration frameworks such as LangGraph, Semantic Kernel, AutoGen, or the Azure AI Agent Service.

• Experience with Model Context Protocol (MCP) servers and tool integration for agents.

• Experience building evaluation and observability tooling specifically for LLM applications.

• Experience with knowledge graphs, graph databases, or ontology tooling such as Neo4j, Cosmos DB Gremlin, RDF, or OWL.

• Experience with Azure data and analytics tooling such as Data Factory, Synapse, Microsoft Fabric, or Databricks.

• Familiarity with Azure integration services: API Management, Logic Apps, Service Bus, Event Hubs.

• Experience with Microsoft Power Platform for custom connectors.

• Background in insurance, financial services, or another regulated industry.

Behavioural Qualities

• Self-directed. Comfortable owning an outcome with limited definition and driving it to production.

• Bias toward shipping working software over producing documents about software.

• Pragmatic about tooling. Picks the approach that delivers and discards it when something better shows up.

• Careful with other people’s production systems. Tests, monitors, and cleans up.

• Keeps current with a fast-moving field and brings useful things back to the team.

Deliverables & Accountabilities

• Production AI agents and services deployed in Azure with tests, telemetry, and documented operational procedures.

• Terraform modules and CI/CD pipelines support repeatable provisioning and deployment of AI workloads.

• Working vendor platform integrations with documented configuration, authentication, and data flows.

• Evaluation suites and quality gates for AI behavior running in CI.

• Curated knowledge bases, domain models, and ingestion pipelines support production of AI retrieval, with documented structure and refresh cadence.

• Design contributions, spikes, and reference implementations feeding into solution designs owned by the AI Lead and architecture.

• Reusable agent-assisted development assets available to the engineering team.

• Observability of dashboards and alerting for the systems you own.

Our sustainability promise

We're on a life-long journey to become an ever-more sustainable group. It's a commitment to taking care of our people and the world we live in, to doing good business, and to making a positive impact wherever we can. Our governance processes, company policies, and review systems are all geared towards our goal of making a positive impact on the world.

Compensation and Benefits
 

The expected base salary range for this role is $150,000 - $170,000; actual base salary will be determined based on factors including candidate experience and work location. This role is classified as exempt under the Fair Labor Standards Act (FLSA).

In addition to a competitive base salary, employees are eligible to receive a discretionary bonus. Howden also offers a variety of benefits and programs, subject to eligibility, including:

Medical, dental, and vision insurance, including healthcare savings and reimbursement accounts

• 401(k) retirement plan
• Flexible Paid Time Off and paid parental leave
• Life and Disability insurance

What do we offer in return?

A career that you define. At Howden, we value diversity – there is no one Howden type. Instead, we’re looking for individuals who share the same values as us:

• Our successes have all come from someone brave enough to try something new

• We support each other in the small everyday moments and the bigger challenges

• We are determined to make a positive difference at work and beyond

Reasonable adjustments

We're committed to providing reasonable accommodations at Howden to ensure that our positions align well with your needs.  Besides the usual adjustments such as software, IT, and office setups, we can also accommodate other changes such as flexible hours* or hybrid working*.

If you're excited by this role but have some doubts about whether it’s the right fit for you, send us your application – if your profile fits the role’s criteria, we will be in touch to assist in helping to get you set up with any reasonable adjustments you may require.

*Not all positions can accommodate changes to working hours or locations. Reach out to your Recruitment Partner if you want to know more.