ML Ops Engineer

Job Title: ML Ops Engineer
Location: Bangalore / Chennai / Gurgaon / Kolkata / Pune / Hyderabad
Experience: 2-4 Years

Job Description:

We are looking for a dynamic and experienced ML Ops Engineer to join our team in Bangalore. The ideal candidate will be responsible for building and managing ML systems in production. You will work closely with our Data Scientists, ML Engineers, and IT teams to maintain and improve our machine learning platforms.

Responsibilities:

1. Develop, test, validate, and maintain robust tools using cloud-based technologies, Python, PySpark, etc. for machine learning (ML) models.
2. Work closely with data scientists and ML engineers to understand their needs and translate them into reliable, efficient, and scalable production systems.
3. Ensure the successful deployment of ML models, including logging, monitoring, and setting up automated pipelines.
4. Leverage advanced programming skills to create data structures and algorithms.
5. Develop and maintain scalable data pipelines and build out new API integrations to support continuing increases in data volume and complexity.
6. Establish robust logging and versioning systems for our ML models.
7. Use orchestration tools to automate ML pipelines.
8. Implement CI/CD pipelines for ML models.
9. Collaborate with cross-functional teams to understand the requirements and implement solutions.
10. Monitor ML models and system performance, ensuring high availability and reliability.
11. Provide technical expertise and best practices for ML productionization.
12. Proactively identify and resolve issues that may impact ML model performance.

Requirements:

1. Bachelors degree in Computer Science, Engineering, or related field.
2. 2-4 years of experience in a similar role.
3. Strong knowledge of Python and PySpark. Experience with other programming languages is a plus.
4. Extensive experience in building and maintaining production systems.
5. Experience with version control/Git, and CI/CD pipelines.
6. Strong understanding of machine learning models.
7. Proficiency in applying advanced programming algorithms.
8. Experience with orchestration tools.
9. Strong problem-solving skills and attention to detail.
10. Excellent communication and collaboration skills.
11. Ability to work in a fast-paced, dynamic environment.