Artificial Intelligence Engineer

Lead AI Engineer Contract to Hire Hybrid (St. Louis or Atlanta) • Must be a USC or GC holder • Our client is looking for someone to come in to be the driving force behind their technical vision and execution as the Lead AI Engineer. If you have experience in Generative AI and deploying AI agents to production environments you should apply! Responsibilities: • Implement Sophisticated AI Agents: Design, build, and deploy complex AI agents using LangChain and LangGraph. You will own the core logic that automates intricate decision-making within the claims lifecycle. • Master Prompt & Context Engineering: Design, test, and refine complex prompts and contextual data frameworks to ensure our AI agents perform with maximum accuracy, efficiency, and reliability. • Lead AI Research & Innovation: Stay at the bleeding edge of AI. You’ll be responsible for identifying, prototyping, and integrating the latest foundational models, RAG techniques, and agentic frameworks to solve unique business challenges. • Build for Production Scale on GCP: Engineer and operate our AI systems in a scalable, reliable production environment on Google Cloud Platform. Your work will directly impact millions of users. • Champion MLOps for Agentic Systems: Establish and lead best practices for the reliability, versioning, monitoring, and observability of our AI agents, using tools like Langfuse to ensure production-grade performance. Requirements: • Bachelor's degree or equivalent experience • 7+ years in software engineering, with a strong track record of technical leadership and shipping complex, scalable systems. • Experience in a dedicated AI/ML role, with hands-on experience in model integration, MLOps, and applying AI to solve business problems. • Direct experience architecting and building solutions with LangChain, LangGraph, or similar agentic AI frameworks. • In-depth experience with Google Cloud Platform (GCP), specifically its AI/ML services (Vertex AI, etc.). • 3+ years of proven experience leveraging Kubernetes workloads. • Proficiency in Python, JavaScript/TypeScript and/or Java and working knowledge of a modern front-end framework (Angular, React, or Vue) to collaborate effectively with UI teams. • Hands-on experience with LLM observability tools like Langfuse for monitoring and debugging agentic workflows. Cloud-Native Proficiency: • Cloud Platforms: Extensive hands-on experience with at least one major cloud provider (AWS, Google Cloud, or Azure). • Containerization: Mastery of Docker for containerizing applications and Kubernetes for orchestration. • Infrastructure as Code (IaC): Proficiency with tools like Terraform or CloudFormation to manage infrastructure programmatically. • CI/CD Tools: Experience with CI/CD tools such as Github Actions, Argo CD, Jenkins • Database Knowledge: Strong experience with both SQL (e.g., Spanned DB, Alloy DB, PostgreSQL, MySQL) and NoSQL (e.g., MongoDB, DynamoDB and Firestore) databases. • Cloud-Native Proficiency: • Cloud Platforms: Extensive hands-on experience with at least one major cloud provider (AWS, Google Cloud, or Azure). • Containerization: Mastery of Docker for containerizing applications and Kubernetes for orchestration. • Infrastructure as Code (IaC): Proficiency with tools like Terraform or CloudFormation to manage infrastructure programmatically. • CI/CD Tools: Experience with CI/CD tools such as Github Actions, Argo CD, Jenkins • Database Knowledge: Strong experience with both SQL (e.g., Spanned DB, Alloy DB, PostgreSQL, MySQL) and NoSQL (e.g., MongoDB, DynamoDB and Firestore) databases.