Ai Engineer Jobs

About The Position

Requirements

• 3+ years of experience in software engineering, AI engineering, machine learning, or a related technical field, with at least 2 years designing and delivering production AI solutions.
• Hands-on experience building enterprise applications using Large Language Models (LLMs), AI memory and context management, AI agents, prompt engineering, and orchestration frameworks.
• Experience with deploying applied AI features and related cloud-native technologies.
• Strong programming skills in Python or JavaScript, with experience developing RESTful APIs, microservices, and scalable distributed applications.
• Experience integrating AI solutions with enterprise systems using REST APIs, event-driven architectures, vector databases, and knowledge repositories.
• Familiarity with modern AI or orchestration frameworks.
• Experience implementing AI evaluation, monitoring, observability, and testing frameworks to measure model quality, reliability, latency, and cost.
• Understanding of Responsible AI principles, security, governance, prompt safety, model access controls, and enterprise AI compliance.
• Experience with Git, GitLab/GitHub, CI/CD pipelines, Infrastructure as Code, and DevOps/MLOps practices for deploying AI applications.
• Experience working with Databricks, SQL Server, or other enterprise data platforms.
• Experience with modern cloud environments such as AWS, Azure, or Google Cloud Platform
• Bachelor's degree in Computer Science, Software Engineering, or equivalent practical experience.
• Demonstrated ability to translate complex business problems into scalable, secure, and production-ready AI solutions while collaborating effectively with cross-functional teams.

Nice To Haves

• Experience developing AI solutions for the property & casualty insurance industry, including underwriting, submissions, or policy administration.
• Experience with Model Context Protocol (MCP), enterprise AI agents, multi-agent systems, vector databases, and modern AI engineering platforms
• Strong communication skills and ability to work cross-functionally with different departments
• Ability to travel up to 10% of the year.

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.