Applied AI Engineer

Mission Underwriting Services is hiring an Applied AI Engineer to build and scale its enterprise AI platform and intelligent automation capabilities. The role develops production-ready AI applications and agentic workflows, integrates AI with enterprise systems, and establishes practices for secure, reliable AI solutions across business functions.

Responsibilities:

• Design, develop, and deploy production-grade AI solutions using large language models (LLMs), AI agents, latest memory and context management patterns, and machine learning techniques
• Build intelligent workflows and AI-powered applications that automate business processes across underwriting, operations, finance, and other enterprise functions
• Develop and maintain scalable AI services using modern cloud-native architectures
• Integrate AI capabilities with enterprise platforms including policy administration, submission management, data platforms, document management, and external data providers
• Design and implement prompt engineering, orchestration frameworks, vector databases, and knowledge retrieval solutions to improve AI performance and reliability
• Evaluate, fine-tune, and optimize foundation models and AI workflows for accuracy, latency, scalability, and cost efficiency
• Develop evaluation frameworks, automated testing, and monitoring capabilities to measure AI quality, hallucination rates, response accuracy, and overall system performance
• Implement AI guardrails, security controls, governance standards, and responsible AI practices to ensure compliant and trustworthy AI solutions
• Collaborate with product owners, business stakeholders, business analysts, and engineers to translate business requirements into scalable AI solutions
• Build solutions including AI models from providers such as Anthropic and OpenAI to solve intelligence problems, automate and streamline processes for the business
• Leverage development augmentation tools such as GitHub Copilot, Claude Code, Codex, and others to learn, plan, design, and build effectively
• Build reusable AI components, SDKs, and engineering frameworks that enable rapid delivery of enterprise capabilities
• Use databases such as MongoDB and Databricks to store and process data
• Optimize AI infrastructure, inference pipelines, and orchestration workflows for high availability, resilience, and operational efficiency
• Stay current with emerging AI technologies, frameworks, and best practices, recommending new capabilities that can enhance Mission's AI strategy
• Support production AI systems by monitoring performance, troubleshooting issues, and continuously improving solution quality and business outcomes