Senior AI Engineer

Howden • Charlotte, NC

1d • $150,000 - $170,000 • Remote

About The Position

This is a hands-on build role on Howden's US AI Engineering team, responsible for building and shipping AI-enabled software. The role involves writing agents, pipelines, integrations, and infrastructure as code to deploy AI capabilities into production on Azure. It is primarily a software engineering role, focusing on production code in Python, Node.js, TypeScript, and .NET (C#), utilizing Azure services and Howden's data. The engineer will work from solution designs and integration patterns provided by the AI Lead and enterprise architecture, owning the implementation end-to-end. A significant portion of the work will involve coding, Terraform, and CI/CD pipelines, with an emphasis on agent-assisted development using coding agents and AI development tooling to enhance productivity. This is a self-directed role where the engineer is expected to break down outcomes, sequence tasks, resolve blockers, and deliver production-quality work in collaboration with 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 maintainable code.
• 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 AI systems: source profiling, extraction, cleansing, normalization, and enrichment across structured and unstructured data.
• Model the domain by defining taxonomies, ontologies, entity and relationship models, and metadata schemas that give agents a consistent view of insurance data.
• 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 identify AI failures (query logs, retrieval hit rates, groundedness scores, cost per interaction, user feedback) and act on findings.
• Write SQL, transformations, and analysis needed to answer data questions independently.
• Use coding agents and AI development tooling (Claude Code, GitHub Copilot, and similar) as a primary part of the daily workflow for implementation, refactoring, test generation, and debugging.
• Build and maintain the scaffolding that enables agent-assisted development on repositories: context files, tool definitions, repository conventions, task decomposition patterns, and reusable prompt assets.
• Apply engineering judgment to agent output, reviewing generated code rigorously and identifying where tooling saves time versus creates cleanup 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.
• 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 built infrastructure.
• 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.
• Ensure environments are reproducible by replacing manual portal creations with 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 users 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 systems to enable others to operate them.
• Implement content filtering, safety guardrails, and Azure AI Content Safety configurations in solutions.
• 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, providing options, trade-offs, cost and latency implications, and recommendations.
• Build spikes and reference implementations to prove or disprove design approaches before team commitment.
• 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, deviations from the original approach, and reasons.
• Work within 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.
• Flag design gaps and integration risks early to the AI Lead and enterprise architecture.
• Write clear technical documentation for owned code and infrastructure.

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
• Discretionary bonus

What This Job Offers

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