Director, AI Solutions Engineer
$155,000–$180,000 year
RemoteUnited States
Job Summary
Build and deploy secure, scalable AI solutions including APIs, agents, and containerized services using AWS ECS, Databricks, and LLM platforms like Claude Code and OpenAI Codex. Design, operationalize, and govern RAG pipelines, knowledge bases, and agentic workflows with robust observability, cost controls, and safety measures. Partner with the Lead AI Solutions Architect and AI Data Engineer to translate business needs into production-ready systems that support underwriting, claims, and member experience. Own incident response, CI/CD standards, and engineering reviews while mentoring the team on AI-assisted development practices.
Required Qualifications
- 7+ years of professional software engineering experience
- at least 3 years building and operating AI/ML systems in production
- Proven hands-on experience with LLMs: prompt engineering, RAG pipelines, fine-tuning or adapting open-source models, function/tool calling, agent orchestration, and working with Claude, OpenAI/Codex, Gemini, or comparable models via API
- Experience building and shipping agentic AI systems, multi-step agents, tool-use orchestration, reusable agent skills, autonomous workflow automation, and governed enterprise integrations in a production environment
- Strong Python engineering skills
- ability to write clean, maintainable, production-grade code with FastAPI or similar frameworks
- ability to package AI capabilities as APIs, services, workers, or containerized applications
- Experience with AI/ML infrastructure: Databricks-based AI/ML workflows, knowledge bases, vector or hybrid search, feature pipelines, model serving patterns, container orchestration, Docker, and cloud-native deployment
- Familiarity with cloud-native AI workloads on AWS — including cost governance and performance tuning at scale
- Experience implementing trust, safety, and governance controls in AI systems: PII handling, content filtering, access controls, and auditability
- Comfort working in a delivery-oriented team: you ship, you measure, you iterate
- Hands-on experience with AI-assisted software engineering tools such as Claude Code, OpenAI Codex, GitHub Copilot, or comparable developer productivity platforms
- Experience creating reusable AI agent artifacts such as skill files, tool definitions, prompt templates, system instructions, evaluation datasets, and guardrail patterns
- Must work onsite at one of our offices
Desired Qualifications
- Background in insurance, fintech, or other regulated industries, with familiarity with compliance frameworks such as SOC 2, NAIC model laws, or GDPR
- Experience with LLM observability tooling: LangSmith, Weights & Biases, Dynatrace LLM monitoring, OpenTelemetry, or equivalent
- Familiarity with ML frameworks (PyTorch, scikit-learn) for classical ML alongside LLM-based approaches
- Experience with fraud detection, document intelligence, risk scoring, or actuarial data systems
- Contributions to open-source AI/ML projects or published work in applied NLP or machine learning
- Experience deploying AI applications as containerized services on AWS ECS or similar cloud-native platforms
- Experience designing and implementing agent skills, tool registries, function-calling interfaces, or reusable agent capabilities for enterprise AI systems
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