Software Engineer - MLOps

About the job : Job Description Summary :

We are seeking a skilled Machine Learning Ops Engineer with 35 years of experience to design, deploy, and maintain scalable machine learning systems in production.

The ideal candidate will have hands-on experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn, along with strong expertise in cloud platforms (AWS, Azure, or GCP) and containerization technologies like Docker and Kubernetes.

Job Description : Key Responsibilities :

• Design, implement and maintain ML pipelines for model training, validation, and deployment.

• Automate model training, testing and deployment (CI/CD of ML models).

• Monitor model performance, data drift, and system health in production environments.

• Deploy models in pre-prod, prod environments.

• Collaborate with data scientists to operationalize machine learning models and algorithms.

• Implement version control for models, datasets, and ML experiments using MLOps tools.

• Optimize ML infrastructure for scalability, reliability, and cost-effectiveness.

• Troubleshoot and resolve issues related to model deployment and production systems.

• Maintain documentation for ML workflows, deployment processes, and system architecture.

• This position may require availability outside of standard business hours as part of a rotational on-call schedule.

What Youll Need to Be Successful (Required Skills) :

• 3 to 5 years of experience in software development, DevOps, or data engineering.

• Proficiency in Python, SQL, and at least one ML framework such as TensorFlow, PyTorch, Scikit-learn.

• Experience with containerization (Docker) and orchestration tools (Kubernetes).

• Knowledge of cloud platforms such as AWS, Azure, GCP and their ML services.

• Understanding of CI/CD pipelines, version control (Git), and infrastructure as code.

• Familiarity with monitoring tools and logging frameworks for production systems.

• Experience with data pipeline tools such as Apache Airflow, Kubeflow, or similar.

• Strong problem-solving skills and ability to work in fast-paced, collaborative environments.

Preferred Skills :

• Experience with MLOps platforms such as MLflow, Weights & Biases, Neptune.

• Knowledge of streaming data processing such as Kafka, Kinesis.

• Familiarity with infrastructure monitoring tools such as Prometheus, Grafana.

• Understanding of model interpretability and explainability techniques.