We are currently seeking a skilled Machine Learning Engineer to a growing company. This role will focus on deploying machine learning models into production environments, ensuring scalability, reliability, and performance. You will be working closely with data scientists and engineers to bring machine learning models from development to production while maintaining and monitoring them for continuous improvements.

The ideal candidate is highly experienced in building and deploying machine learning models, with a deep understanding of the tools and infrastructure necessary to ensure seamless integration of models into production systems.

Required Skills & Experience:

Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or related field. At least 5 years of experience in machine learning and model deployment. Strong proficiency in Python and libraries such as TensorFlow, PyTorch, and Scikit-learn. Experience with MLOps tools such as MLflow, Kubeflow, or TFX. Hands-on experience deploying models using Docker and Kubernetes. Familiarity with CI/CD pipelines for automating model deployment. Experience working with cloud platforms such as AWS (SageMaker), Google Cloud (AI Platform), or Azure (ML Studio). Strong understanding of APIs and integrating ML models into applications. Experience with monitoring and maintaining models in production.

Desired Skills & Experience:

Experience with real-time model serving (e.g., TensorFlow Serving, TorchServe). Knowledge of model optimization techniques for production (e.g., ONNX, TensorRT). Familiarity with distributed systems and model scaling. Experience with A/B testing and model performance evaluation in production environments.

What You Will Be Doing:

Tech Breakdown:

50% Model Deployment & Monitoring 30% Model Training & Optimization 20% Collaboration with Data Science & Engineering Teams

Daily Responsibilities:

60% Hands-On Deployment 30% Collaborating with Engineering and Data Teams 10% Monitoring and Maintaining Production Models