Builds with LLMs, AI agents, prompt engineering and developer AI tools (e.g., GitHub Copilot); focused on applied generative AI in production.
About the Role
The Applied AI Engineer will design, build, and scale production AI solutions and agentic workflows using LLMs and cloud-native architectures to automate underwriting and enterprise processes. The role focuses on integrating models, vector databases, and enterprise systems while establishing engineering best practices, governance, and operational monitoring across business functions.
Job Description
Role
The Applied AI Engineer will design, implement, and scale Mission’s enterprise AI platform and intelligent automation capabilities. This hands-on engineering role delivers production-ready AI applications, agentic workflows, and integrations that enable AI-powered experiences across underwriting, operations, finance, HR, and other enterprise functions.
Key Responsibilities
- Design, develop, and deploy production-grade AI solutions using LLMs, AI agents, memory/context management patterns, and machine learning techniques.
- Build intelligent workflows and AI-powered applications to automate business processes across underwriting, operations, finance, and other functions.
- Develop and maintain scalable AI services using cloud-native architectures and optimize inference pipelines and orchestration for availability and cost efficiency.
- Integrate AI capabilities with enterprise platforms (policy administration, submission management, data platforms, document management, external data providers).
- Design and implement prompt engineering, orchestration frameworks, vector database integrations, and knowledge retrieval solutions.
- Evaluate, fine-tune, and optimize foundation models and AI workflows for accuracy, latency, scalability, and cost.
- Develop evaluation frameworks, automated testing, monitoring, and observability to measure model quality, hallucination rates, and system performance.
- Implement AI guardrails, security controls, governance standards, and Responsible AI practices.
- Build reusable AI components, SDKs, and engineering frameworks to accelerate delivery of enterprise capabilities.
- Support production AI systems through monitoring, troubleshooting, and continuous improvement.
Technical Environment (examples cited)
- Models and providers referenced: Anthropic, OpenAI
- Developer/augmentation tools: GitHub Copilot, Claude Code, Codex
- Datastores and platforms referenced: MongoDB, Databricks, SQL Server, vector databases
- Cloud and infra: AWS, Azure, Google Cloud Platform, CI/CD, Infrastructure as Code
- Integration patterns: REST APIs, event-driven architectures, microservices
Requirements
- 3+ years in software engineering, AI engineering, machine learning, or a related technical field, including at least 2 years designing and delivering production AI solutions.
- Hands-on experience with LLMs, AI memory/context management, AI agents, prompt engineering, and orchestration frameworks.
- Experience deploying applied AI features and cloud-native technologies.
- Strong programming skills in Python or JavaScript; experience developing RESTful APIs, microservices, and distributed applications.
- Experience integrating AI solutions with enterprise systems via REST APIs, event-driven patterns, vector DBs, and knowledge repositories.
- Familiarity with AI evaluation, monitoring, observability, testing frameworks, and Responsible AI principles (security, governance, prompt safety, access controls).
- Experience with Git, GitLab/GitHub, CI/CD pipelines, Infrastructure as Code, and DevOps/MLOps practices.
- Experience with Databricks, SQL Server, or other enterprise data platforms.
- Experience with AWS, Azure, or Google Cloud Platform.
- Bachelor’s degree in Computer Science, Software Engineering, or equivalent experience.
- Ability to translate complex business problems into scalable, secure, production-ready AI solutions and collaborate cross-functionally.
Preferred Qualifications
- Experience developing AI solutions for property & casualty insurance (underwriting, submissions, policy administration).
- Familiarity with Model Context Protocol (MCP), enterprise AI agents, multi-agent systems, and modern AI engineering platforms.
- Strong communication skills and cross-functional collaboration experience.
- Ability to travel up to 10% annually.
Benefits
- Remote-first work environment (US-based)
- Medical, dental, and vision
- 401(k) and 401(k) match
- Life and disability benefits
- Unaccrued paid time off
- 11 paid holidays
- Employee discounts
- Employee assistance program
- Educational assistance program
- Employee referral program
- Paid parental leave
Working Conditions & Location
- Remote position; occasional planned in-office activities (typically 2–4 times per year). Must reside in the United States and be authorized to work in the U.S. without sponsorship.
- Standard office ergonomics: extended computer work, regular use of hands, frequent talking/hearing, and normal vision requirements.