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.

- Experience with feature stores and data versioning tools.

- Certification in cloud platforms such as AWS ML, Azure AI, GCP ML.

Education/ Certifications :

- Bachelor's degree in computer science engineering, Information Technology engineering or any engineering related field.