Machine Learning Engineer

What Youll Do

As an MLOps Engineer, you will play a critical role in ensuring the operationalization and automation of machine learning models. Your day-to-day will include:

• Operationalizing ML Models: Deploy and manage ML models in production, ensuring they meet scalability, reliability and performance requirements.
• Collaboration with Data Science Teams: Work alongside data scientists to move models from development to production, offering expertise in deployment strategies and best practices.
• Optimizing ML Pipelines: Design and streamline ML pipelines for efficient model training, testing and deployment. Implement CI/CD processes to automate and enhance the ML lifecycle.
• Managing Cloud Environments: Configure and manage cloud environments (AWS, Azure, or GCP) to support ML operations, ensuring they integrate smoothly with other systems.
• Implementing Monitoring Tools: Create monitoring solutions to track model performance, detect anomalies and ensure continuous accuracy.
• Providing Technical Support: Troubleshoot and resolve issues related to ML models in production, ensuring smooth operation and optimal performance.

What Makes You a Fit

Academic Background: A Bachelors degree in Engineering, Computer Science, Mathematics, or a related quantitative discipline from a top-tier institution.

Technical Expertise:

• A minimum of 3 years of experience in ML Ops, Data Engineering, or related fields, with hands-on experience deploying and managing ML models in production.
• Proficient with leading ML and data tools such as MLflow, Spark, PyTorch, and others.
• Strong understanding of cloud platforms (AWS, Azure, or GCP) and experience managing scalable ML environments.
• Familiarity with CI/CD pipelines, containerization (e.g., Docker) and orchestration tools (e.g., Airflow, Jenkins).

Extra Skills:

• Excellent problem-solving skills for addressing complex technical challenges.
• Strong communication and interpersonal skills, with the ability to collaborate effectively across teams.
• Ability to thrive in a fast-paced environment and navigate ambiguity.
• Strong understanding of technology stacks and how data flows across systems.
• Highly adaptable with the ability to influence and negotiate in collaborative settings.