machine learning engineer

One of our staffing partners is looking for one of its clients a Machine Learning Engineer with strong data engineering skills to help bridge the gap between raw data and intelligent decision-making.

Role Overview:
You ll be responsible for designing, developing, and deploying machine learning models while also building and maintaining the data infrastructure that powers them. This hybrid role is perfect for someone who thrives at the intersection of ML and data engineering.

Key Responsibilities:

• Build and optimize data pipelines for training and inference workflows
• Develop, train, and deploy machine learning models using frameworks like TensorFlow, PyTorch, or Scikit-learn
• Collaborate with data scientists, analysts, and product teams to define data requirements and model objectives
• Implement model monitoring, versioning, and retraining strategies
• Ensure data quality, lineage, and governance across ML pipelines

Qualifications:

• Bachelor s or Master s degree in Computer Science, Data Science, or related field
• 2+ years of experience in machine learning
• Leverage MLOps tools such as MLflow, Kubeflow, SageMaker, or Vertex AI for model lifecycle management
• Proficiency in Python and SQL; experience with Spark, Airflow, or similar tools
• Strong understanding of ML lifecycle, from data preprocessing to model deployment
• Experience with cloud platforms (AWS, Google Cloud Platform, or Azure) and containerization (Docker, Kubernetes)

Preferred Skills:

• Familiarity with MLOps tools (MLflow, SageMaker, Vertex AI)
• Experience with real-time data processing (Kafka, Flink)
• Knowledge of feature stores and model registries
• Exposure to CI/CD practices for ML pipelines