Demonstrates expertise in designing and deploying production-grade AI solutions using Large Language Models (LLMs), prompt engineering, and orchestration frameworks. Proficient in integrating AI capabilities with enterprise systems while ensuring compliance with Responsible AI principles and optimizing for performance and cost efficiency.

Highest-signal resume keywords

Production AI Solutions DevelopmentLarge Language Models (LLMs)Prompt EngineeringCloud-Native TechnologiesAI Evaluation and Monitoring Frameworks

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformJavaScriptMicroservicesMongoDBPythonSQL

About the role

Key responsibilities & impact

Design, develop, and deploy production-grade AI solutions using LLMs, AI agents, 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 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
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 for AI quality, hallucination rates, response accuracy, and system performance
Implement AI guardrails, security controls, governance standards, and responsible AI practices
Collaborate with product owners, business stakeholders, business analysts, and engineers to translate requirements into scalable AI solutions
Build AI models using providers such as Anthropic and OpenAI to solve intelligence problems and streamline business processes
Leverage GitHub Copilot, Claude Code, Codex, and other development augmentation tools
Build reusable AI components, SDKs, and engineering frameworks
Use MongoDB and Databricks to store and process data
Optimize AI infrastructure, inference pipelines, and orchestration workflows for availability, resilience, and operational efficiency
Stay current with emerging AI technologies, frameworks, and best practices
Monitor production AI systems, troubleshoot issues, and continuously improve solution quality and business outcomes

Requirements

What you’ll need

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
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
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
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 practical experience
Demonstrated ability to translate complex business problems into scalable, secure, and production-ready AI solutions while collaborating effectively with cross-functional teams
Preferred: Experience developing AI solutions for the property & casualty insurance industry
Preferred: 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
Must live in the United States
Must be authorized to work in the United States without requirement of employment sponsorship/visa
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

Benefits

Comp & perks

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

ATS Keywords

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Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

PythonJavaScriptRESTful APIsMicroservicesAI Memory and Context ManagementOrchestration FrameworksAI AgentsDatabricksSQL ServerModel Context Protocol (MCP)

Soft Skills

Strong Communication SkillsCross-Functional Collaboration

Tools & Technologies

GitGitLabGitHubAWSAzureGoogle Cloud PlatformCI/CD PipelinesInfrastructure as CodeDevelopment Augmentation ToolsVector Databases

Industry Keywords

Enterprise AI ComplianceResponsible AI PrinciplesProperty & Casualty Insurance IndustryAI Quality MonitoringEvent-Driven Architectures