Machine Learning Engineer

Dearborn, MI (HYBRID/ONSITE) W2 CONTRACT ONLY Overview We are seeking an experienced AI Engineer to design, build, and deploy scalable AI solutions leveraging Machine Learning, LLMs, and Agentic AI. This role focuses on delivering production-ready systems that drive automation, efficiency, and measurable business impact within a cloud-based environment. Key Responsibilities • Develop and deploy ML models (predictive, optimization, Generative AI) • Build end-to-end AI pipelines (data ingestion ? modeling ? deployment ? monitoring) • Design LLM-based applications (RAG, prompt orchestration, agentic workflows, tool integrations) • Create APIs and AI services for enterprise integration • Implement MLOps practices (CI/CD, model monitoring, retraining) • Optimize systems for scalability, performance, and reliability • Partner with stakeholders to translate business needs into AI solutions Required Qualifications • Bachelor’s degree in Computer Science, Data Science, or related field • 6+ years IT experience; 4+ years in development • Strong Python expertise (APIs, backend, automation) • Experience deploying ML models in production • Hands-on with LLMs, prompt engineering, Generative AI • Experience with AWS or GCP (BigQuery preferred) • Proficiency in ML frameworks (Scikit-learn, TensorFlow, PyTorch) • Strong understanding of SDLC, testing, and deployment practices Preferred / Nice To Have • Experience with Agentic AI, autonomous agents, tool-calling architectures • Familiarity with LangChain, LlamaIndex, CrewAI, AutoGen • MLOps tools: MLflow, Airflow, Vertex AI, SageMaker, Kubeflow • Docker, Kubernetes, and cloud-native architecture • Knowledge of vector databases, embeddings, RAG, semantic search • Experience with large-scale data environments, ETL pipelines, data lakes Core Skills • Python, SQL • Machine Learning & Deep Learning • LLMs, RAG, Prompt Engineering, Embeddings • Agentic AI / AI Agents • Cloud (AWS or GCP) • API Development (FastAPI / Flask) • MLOps, CI/CD, Model Deployment & Monitoring