Senior AI Engineer

Howden • Charlotte, NC

5d • $150,000 - $170,000 • Remote

About The Position

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.

Requirements

• 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.

Nice To Haves

• 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.

Responsibilities

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Benefits

• 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 This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Associate degree