We are seeking a highly skilled and experienced Machine Learning Engineer with deep expertise in Python and extensive hands-on experience working with leading ML frameworks such as TensorFlow, PyTorch, and Scikit-learn. The ideal candidate will bring a strong foundation in developing, deploying, and scaling end-to-end machine learning solutions within modern DevOps and cloud-native environments.

Find out exactly what skills, experience, and qualifications you will need to succeed in this role before applying below. Core Competencies: Expertise in Python Programming: Proficient in writing high-performance, maintainable, and modular Python code tailored for data science and machine learning applications. Advanced ML Framework Experience: Proven experience in designing and implementing machine learning models using industry-standard libraries such as TensorFlow , PyTorch , and Scikit-learn . Strong understanding of supervised, unsupervised, and deep learning techniques. DevOps and MLOps Engineering: Demonstrated expertise in setting up and managing CI/CD pipelines for ML model development and deployment. Skilled in leveraging containerization tools (Docker, Kubernetes) to orchestrate scalable ML workloads. Experience with infrastructure-as-code (IaC) tools like Terraform and Ansible to automate and manage infrastructure efficiently. Cloud-Native ML Architecture: Hands-on experience with cloud platforms such as Google Cloud Platform (GCP) and Microsoft Azure . Comfortable deploying ML workloads in cloud-native environments, and implementing cloud services for compute, storage, networking, and AI/ML APIs. Deep understanding of ML lifecycle automation and orchestration using tools such as Kubeflow , MLflow , Apache Airflow , and TensorFlow Extended (TFX) . Capable of architecting robust pipelines for data ingestion, model training, validation, deployment, and monitoring. Monitoring and Observability for ML Workloads: Skilled in implementing observability best practices including logging, metrics, and alerting for production ML models. Familiar with tools and frameworks that enable monitoring of model performance and system health in real time. Preferred Skills and Qualifications (Nice to Have): Distributed Computing Expertise: Familiarity with distributed data processing and parallel computing frameworks like Apache Spark , Ray , or Dask to scale data science workflows efficiently. Responsible AI and Model Governance: Understanding of key aspects of model interpretability , fairness , and bias detection using tools such as SHAP , LIME , or Fairlearn . Experience integrating these tools into ML workflows is highly desirable. Security and Compliance in ML Systems: Strong awareness of security best practices across the ML lifecycle. This includes data encryption , secure APIs , access control , and governance frameworks to ensure compliance and data protection. This position requires a forward-thinking engineer who is passionate about building scalable and secure ML systems that are production-grade, resilient, and responsible. If you're enthusiastic about bridging the gap between data science and reliable engineering practices, we’d love to hear from you. Interview Process: Mandatory Glider test followed by Interview Seniority level Seniority levelMid-Senior level Employment type Employment typeContract Job function Job functionEngineering and Information Technology IndustriesIT Services and IT Consulting Referrals increase your chances of interviewing at IntelleSphere LLC by 2x Get notified about new Machine Learning Engineer jobs in Sunnyvale, CA . 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