MLOps Engineer
MLOps Engineer
🚀 HIRING: MLOps Engineer
Ledelsea is hiring an MLOps Engineer to deliver end-to-end production ML systems.
📌 Experience Required: Bachelor's + 3-4 years OR Master's + 4-5 years of professional experience spanning software engineering and machine learning operations.
📍 Location Requirement: Candidates must be willing to relocate to the state and city of the client site. This is mandatory for this role.
🛂 Work Authorization: We accept CPT and OPT candidates. Ledelsea also sponsors H1B visas after a minimum of 1 year working with us.
⚠️ Please review the minimum requirements carefully before applying. Profiles that do not meet the experience threshold will not be considered and will be auto-rejected during initial screening. We appreciate your understanding.
Roles & Responsibilities: ✅ Deliver end-to-end production machine learning projects from model handoff through deployment and ongoing operations ✅ Build and maintain ML platforms and infrastructure for training, serving, and monitoring models at scale ✅ Develop and operate model serving solutions using Docker and Kubernetes ✅ Build CI/CD pipelines for ML including automated testing, model validation gates, and progressive deployment ✅ Develop in Java, Python, and SQL, leveraging ML libraries including scikit-learn, XGBoost, TensorFlow, and PyTorch ✅ Build and orchestrate data pipelines using Airflow and Spark to support training and inference workflows ✅ Implement model monitoring including data drift, concept drift, performance degradation, and operational metrics ✅ Establish ML feature management, experiment tracking, and model registry practices ✅ Apply software engineering principles and DevOps best practices to ML workloads ✅ Manage stakeholder relationships across data science, engineering, product, and business teams ✅ Document architectures, runbooks, and on-call procedures
Minimum Qualifications: 🎓 Bachelor's + 3-4 years OR Master's + 4-5 years of relevant experience 💻 Strong Java, Python, and SQL skills 🤖 Hands-on with scikit-learn, XGBoost, TensorFlow, PyTorch 🐳 Proficient with Docker, Kubernetes, and cloud platforms 🌬️ Hands-on experience with Airflow, Spark, and MLOps frameworks 🤝 Strong stakeholder management and communication