Applied AI Engineer

(united States)Remotemid

Machine Learning EngineerData

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Quick Summary

Key Responsibilities

Remote First Work Environment (US Based) Medical, Dental, Vision 401K,

Technical Tools

Machine Learning EngineerData

Responsibilities

~1 min read

→Remote First Work Environment (US Based)

→Medical, Dental, Vision

→401K, 401K 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

About the Role

~1 min read

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.

Requirements

~2 min read

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.

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.

Requires a quiet work environment with regular sitting at a computer for extended periods of time.
Regular use of hands and fingers.
Frequent talking or hearing.
Vision requirements: Close, distance, color, peripheral, focus and depth perception.