AI/ML Engineer responsible for building and deploying production-grade AI and multi-agent systems.
Develop LLM-based applications, AI algorithms, RAG pipelines, and intelligent automation solutions.
Build multi-agent workflows using LangGraph, CrewAI, LlamaIndex, or equivalent frameworks.
Develop backend services using Python, FastAPI, or Flask with async/concurrent programming.
Design and productionize RAG systems using embeddings, vector databases, chunking, retrieval, and reranking.
Deploy AI services on Google Cloud Platform (GCP) using technologies such as BigQuery, Cloud Run/GKE, Vertex AI, and Pub/Sub.
Implement LLM evaluation, observability, tracing, guardrails, and prompt-injection protection.
Build secure NL-to-SQL solutions with validated, least-privilege SQL execution.
Implement CI/CD, Docker, Kubernetes, and infrastructure automation for production deployments.
Optimize AI model cost, latency, and performance through model routing, caching, and tiered processing.
Collaborate with data scientists to convert AI/ML prototypes into scalable and monitored production services.
4+ days onsite/hybrid work environment.
Design and deploy production-grade multi-agent AI architectures using LangGraph, CrewAI, LlamaIndex, or equivalent frameworks.
Build multi-step, stateful agent workflows with orchestration, checkpointing, and tool integration.
Develop RAG pipelines including chunking, embeddings, hybrid retrieval, reranking, and retrieval evaluation.
Build and integrate specialized AI agents such as NL-to-SQL, visualization, RCA/RAG, reporting, and notification agents.
Develop backend AI services using Python, FastAPI, and Flask with asynchronous and concurrent programming.
Integrate BigQuery and ensure secure, validated, least-privilege execution of AI-generated SQL.
Deploy and manage AI services on GCP Cloud Run, GKE, Vertex AI, and Pub/Sub.
Build CI/CD pipelines, containerized services, and infrastructure automation using Docker, Kubernetes, and Cloud Build/GitHub Actions.
Implement LLM evaluation and observability using golden datasets, LLM-as-judge scoring, tracing, and regression monitoring.
Develop guardrails, output validation, prompt-injection defenses, and human-in-the-loop approval workflows.
Optimize cost, latency, and model performance through model routing, caching, and tiered AI pipelines.
Integrate validated AI outputs with operational systems, notifications, ticketing, and reporting platforms.
Collaborate with data scientists to productionize AI/ML prototypes into scalable and monitored services.
Establish testing, versioning, canary/shadow deployments, and safe rollout practices for AI systems.
| Location | Dearborn, MI |
| Job Type | Contractor |
| Salary | $60–$70 Per Hour |
Required Qualifications
Bachelor’s degree in Computer Science, Software Engineering, or related field.
3+ years of production software development experience.
1–2+ years of experience building ML/AI or LLM-based applications.
Proven experience deploying multi-agent or multi-service architectures in production.
Strong Python and backend development experience.
Hands-on experience with agent orchestration frameworks.
Strong experience with RAG, vector databases, embeddings, and retrieval systems.
Experience with GCP/cloud deployment, SQL, cloud data warehouses, Docker, Kubernetes, and CI/CD.
Experience with LLM evaluation, observability, tracing, and AI safety practices.
Must-Have Skills
Python
LLM / Generative AI
Multi-Agent AI
LangGraph / CrewAI / LlamaIndex
RAG
GCP
BigQuery
Cloud Run / GKE / Vertex AI
FastAPI / Flask
SQL
Vector Databases
Docker / Kubernetes
CI/CD
LLM Evaluation & Observability
AI Guardrails & Prompt-Injection Defense
Preferred Skills
Model routing and AI cost optimization
Human-in-the-loop workflows
MCP (Model Context Protocol)
Automotive / EV / IoT / connected-vehicle data
Startup or 0-to-1 product development experience
Salesforce and operational system integrations
Canary/shadow deployment strategies
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