ML Engineer

Location: Remote (USA)
Employment Type: Contract (W2 Preferred)

Job Summary

We are seeking an experienced ML Engineer experience to build, deploy, and operate production-grade machine learning solutions. You'll work across MLOps, model serving, feature engineering, LLM evaluation, and enterprise AI applications in a cloud-native environment.

Key Responsibilities

• Build and maintain ML training pipelines, feature engineering workflows, and experiment tracking.

• Develop model serving infrastructure using REST APIs (FastAPI/Flask), batch inference, and cloud-native deployment patterns.

• Implement MLOps practices including MLflow, model registry, versioning, and automated retraining.

• Monitor model performance, drift, reliability, and production health.

• Collaborate with AI Engineers to integrate ML models into LLM and agentic AI workflows.

• Partner with Data Engineers to ensure high-quality training data and reliable feature pipelines.

• Contribute to LLM fine-tuning, prompt engineering, and model evaluation.

• Document model behavior, monitoring thresholds, and production best practices.

Required Skills

• 2+ years of Machine Learning Engineering experience with production ML models.

• 6+ years of Data Engineering, Data Science, or Software Engineering experience.

• Strong Python programming skills.

• Experience with scikit-learn, XGBoost, PyTorch, or TensorFlow.

• Pandas and NumPy.

• MLflow, Weights & Biases, or similar MLOps platforms.

• Model evaluation, validation, and production monitoring.

• AWS SageMaker, Azure ML, Vertex AI, or similar cloud ML platforms.

• FastAPI or Flask for model serving.

• SQL and data engineering fundamentals.

Preferred Skills

• LLM fine-tuning (LoRA, PEFT).

• Prompt Engineering and LLM Evaluation.

• RAGAS, DeepEval, or similar evaluation frameworks.

• Feature Stores (Feast, Tecton, Snowflake Feature Store).

• Docker, Airflow, Kubeflow, or AWS Step Functions.

• AWS Machine Learning Specialty, Google Professional ML Engineer, or similar certifications.

• Financial Services, Risk Modeling, Fraud Detection, or NLP/Document Intelligence experience.

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