Elanco - MLOps Engineer

Role Overview :

We are looking for a passionate MLOps Engineer to bridge the gap between data science and production deployment.

You will design and manage automated pipelines that enable rapid development, testing, deployment, and monitoring of machine learning models.

This role requires expertise in Python, MLOps frameworks, and databases, with a strong focus on scalability and automation.

Key Responsibilities :

• Build, deploy, and maintain MLOps pipelines for machine learning models in production.
• Automate model training, evaluation, deployment, and retraining processes.
• Integrate ML models into scalable microservices or APIs for consumption.
• Implement model monitoring to track accuracy, drift, and performance degradation.
• Manage model governance, versioning, and reproducibility.
• Collaborate with data scientists to transition research models into production-ready solutions.
• Design CI / CD workflows for ML pipelines leveraging Git, Docker, and Kubernetes.
• Optimize model serving infrastructure for low-latency, high-throughput Skills & Qualifications :
• Strong programming skills in Python and experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow, Seldon).
• Knowledge of SQL / NoSQL databases for model data management.
• Familiarity with cloud platforms (Azure / AWS / GCP) and containerization (Docker, Kubernetes).
• Understanding of monitoring tools (Prometheus, Grafana) for ML Skills :
• Experience with feature stores, model registries, and real-time ML pipelines.
• Exposure to big data processing (Spark, Kafka).
• Knowledge of DevOps practices applied to ML workflows

(ref : hirist.tech)