Applied AI Engineer

Vibehackers; Northern (KY); Hybrid; USD 130,000 - 180,000; Full time

Benefits offered by this job

Medical, dental, and vision

401(k) and 401(k) match

Life and disability benefits

Paid holidays

Employee assistance program

Educational assistance program

Employee referral program

Paid parental leave

Job summary

Vibehackers is seeking an Applied AI Engineer to design, build, and scale production AI solutions and agentic workflows using LLMs and cloud-native architectures. You will automate underwriting and enterprise processes, integrating models, vector databases, and enterprise systems to deliver AI-powered experiences across multiple functions.

The role emphasizes governance, security, observability, and scalable services, with a remote-first US-based setup and occasional in-office activities.

Qualifications

• 3+ years in software/AI engineering with production AI deliverables.
• Hands-on with LLMs, memory management, AI agents, and orchestration.
• Experience deploying AI features via cloud-native tech and REST APIs.
• Familiarity with monitoring, observability, testing, and Responsible AI.
• GIT/GitHub/GitLab, CI/CD, and DevOps/MLOps practices.
• Experience with enterprise data platforms and cloud providers.
• Bachelor’s degree in CS or equivalent.

Responsibilities

• Design, implement, and scale production AI solutions and agentic workflows.
• Develop AI-powered applications automating underwriting and operations.
• Integrate AI with policy administration, data platforms, and document systems.
• Build reusable AI components, SDKs, and engineering frameworks.
• Establish governance, security controls, monitoring, and guardrails.
• Ensure performance, cost efficiency, and reliability of AI services.

Skills

Prompt Engineering

AI Engineering

Machine Learning

Cloud-native Architecture

DevOps/MLOps

API Development

Microservices

Event-driven Architecture

Vector Database Integration

Data Platform Integration

Monitoring & Observability

Security & Governance

Responsible AI

Collaboration

Communication

Scalability & Performance

Software Development

Education

Bachelor's degree in Computer Science

Tools

GitHub Copilot

Claude Code

Codex

MongoDB

Databricks

SQL Server

Vector databases

AWS

Azure

Google Cloud Platform

CI/CD

Infrastructure as Code

Job description

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

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.
• 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.
• 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 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.

Skills

Prompt Engineering AI Engineering Machine Learning Cloud-native Architecture DevOps/MLOps API Development Microservices Event-driven Architecture Vector Database Integration Data Platform Integration Monitoring & Observability Security & Governance Responsible AI Model Evaluation & Testing Collaboration Communication Scalability & Performance Optimization Software Development