Machine Learning Engineer

Machine Learning Engineer (ML Engineer) Location: Remote (USA) Job Type: Contract (W2/C2C/1099) Experience: 4+ Years Job Summary We are seeking a highly skilled Machine Learning Engineer to design, develop, deploy, and optimize scalable machine learning solutions. The ideal candidate will have experience building end-to-end ML pipelines, deploying models into production, and working with cloud platforms and modern MLOps tools. The role bridges data science and software engineering by transforming ML models into reliable production systems. Key Responsibilities • Design, build, and deploy machine learning models for production environments. • Develop scalable data pipelines for training and inference. • Train, validate, and optimize ML and deep learning models. • Build REST APIs and microservices for ML model serving. • Monitor model performance, drift, and retraining pipelines. • Collaborate with Data Scientists, Data Engineers, and Software Engineers. • Optimize feature engineering and model performance. • Implement CI/CD pipelines for machine learning workflows. • Work with cloud-native ML services and containerized deployments. • Document ML architecture, experiments, and deployment processes. Required Skills • Strong programming experience in Python. • Experience with Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM. • Strong knowledge of supervised, unsupervised, and reinforcement learning. • Experience with NLP, Computer Vision, or Time Series forecasting. • SQL and NoSQL database experience. • Experience with Docker and Kubernetes. • Cloud experience with AWS, Azure, or Google Cloud Platform. • Familiarity with MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, or Azure ML. • Strong understanding of statistics, probability, and linear algebra. • Experience with Git, CI/CD, and software engineering best practices. Preferred Qualifications • Bachelor's or Master's degree in Computer Science, Data Science, AI, or a related field. • Experience with Generative AI, LLMs, LangChain, or RAG architectures. • Experience with vector databases (Pinecone, Weaviate, ChromaDB, FAISS). • Knowledge of MLOps and model governance. • AWS, Azure, or GCP certifications are a plus.