ML Engineer | 6 to 9 years | Pan India

Job Description

Design, implement, and maintain end-to-end MLOps pipelines for deploying machine learning models into production.

Collaborate with data scientists to understand model requirements and ensure smooth deployment and integration.

Develop and manage infrastructure for model training, validation, deployment, monitoring, and retraining.

Implement CI/CD pipelines to streamline the deployment and updates of AI/ML models.

Ensure the scalability, reliability, and performance of deployed models through continuous monitoring and optimization.

Utilize containerization and orchestration tools (e.g., Docker, Kubernetes) to manage model deployment environments.

Work with cloud platforms (AWS, GCP, Azure) to deploy and manage AI/ML services.

Implement security best practices for AI/ML models and data pipelines.

Troubleshoot and resolve issues related to model deployment and operation.

Stay updated with the latest MLOps tools, frameworks, and methodologies.

Document processes, configurations, and procedures to ensure knowledge sharing and continuity.

Primary Skills

Proven experience (4+ years) in deploying machine learning models in production environments.

Strong understanding of machine learning, deep learning, NLP, and generative AI techniques.

Proficiency with MLOps tools and frameworks such as MLflow, Kubeflow, TensorFlow Extended (TFX), or similar.

Experience with CI/CD tools such as Jenkins, GitLab CI, or CircleCI.

Proficiency in programming languages such as Python and familiarity with ML/DL frameworks like TensorFlow, PyTorch, and scikit-learn.

Experience with cloud platforms (AWS, GCP, Azure) and their AI/ML services.

Knowledge of containerization and orchestration tools (Docker, Kubernetes).

Strong understanding of version control systems (e.g., Git) and collaborative development workflows.

Excellent problem-solving skills and the ability to design robust, scalable MLOps solutions.

Strong communication skills, with the ability to collaborate effectively with cross-functional teams.