Job Description
Job Description Our client is a custom software development company out of Grand Rapids, Michigan, that exists to make software that humans actually like to use.
Type: P/T Contract 20-40 hours/week to start
Duration: 2 months to start
Location: West MI (Prefer hybrid, will look at remote)
Role Overview: The Technical AI Enablement Engineer leads the architectural implementation of AI across the enterprise. Unlike a traditional software engineer, your focus is specifically on the orchestration layer -building RAG pipelines, managing API integrations, and developing middleware that enables business units to leverage LLMs safely, efficiently, and at scale.
Key Responsibilities: • Design and deploy AI agents using frameworks such as LangChain, LlamaIndex, or AutoGPT to automate complex, multi-step business logic.
• Build and maintain Retrieval-Augmented Generation pipelines, including managingvector databases (e.g., Pinecone, Weaviate, or Milvus) and document ingestion workflows.
• Develop robust Python or Node.js middleware to connect frontier models (OpenAI, Anthropic, Gemini) with internal legacy databases and CRM systems.
• Implement "LLM-as-a-judge" frameworks and automated testing suites to measure model accuracy, latency, and hallucination rates in production.
• Establish technical guardrails for PII masking, prompt injection mitigation, and tokencost optimizationacross all internal applications.
• Guide the selection of hosting environments (e.g., AWS Bedrock, Azure AI Studio) and manage model versioning and deployment cycles.
Job Requirements Required Technical Skills: • Professional proficiency in Python (specifically for data processing and AI backends) and TypeScript/JavaScript.
• Deep experience with LangGraph or Haystack for building stateful, multi-agent workflows.
• Strong SQL skills and experience working with Vector Databases and unstructured data ETL processes.
• Working knowledge of RESTful API design, Webhooks, and authentication protocols (OAuth, API Keys).
• Experience with Git/GitHub, containerization (Docker), and CI/CD pipelines for AIpowered applications.2
• Prompt Engineering (Technical): Mastery of advanced techniques including ReAct prompting, chain-of-thought, and algorithmic prompt optimization.
Experience & Qualifications: • 4+ years in Software Engineering, Data Engineering, or Solutions Architecture.
• 2+ years of hands-on experience building and deploying LLM-based applications in a production environment.
• Education: BS/MS in Computer Science, Data Science, or a related technical field.
• Portfolio: Ability to demonstrate a github repository or technical project involving
autonomous agents or a complex RAG system
Job Tags
Contract work, Remote work