Model Deployment and Management:

- Drive ML prototypes into production ensuring seamless deployment and management on cloud at scale.
- Monitor real-time performance of deployed models, analyze data, and proactively address performance issues.
- Troubleshoot and resolve production issues related to ML model deployment, performance, and scalability.

Collaboration and Integration:

- Collaborate with DevOps engineers to manage cloud compute resources for ML model deployment and performance optimization.
- Work closely with ML scientists, software engineers, data engineers, and other stakeholders to implement best practices for MLOps, including CI/CD pipelines, version control, model versioning, and automated deployment.

Innovation and Continuous Improvement:

- Stay updated with the latest advancements in MLOps technologies and recommend new tools and techniques.
- Contribute to the continuous improvement of team processes and workflows.
- Share knowledge and expertise to promote a collaborative learning environment.

Development and Documentation:

- Build software to run and support machine-learning models.
- Develop and maintain documentation, standard operating procedures, and guidelines related to MLOps processes.
- Participate in fast iteration cycles and adapt to evolving project requirements.





Business Solutions and Strategy:

- Propose solutions and strategies to business challenges.
- Collaborate with Data Science team, Front End Developers, DBA, and DevOps teams to shape architecture and detailed designs.

Mentorship:

- Conduct code reviews and mentor junior team members.
- Foster strong interpersonal skills, excellent communication skills, and collaboration skills within the team.

Mandatory Skills:

- Programming Languages: Proficiency in Python (3.x) and SQL.
- ML Frameworks and Libraries: Extensive knowledge of ML frameworks, libraries, data structures, data modeling, and software architecture.
- Databases: Proficiency in SQL and NoSQL databases.
- Mathematics and Algorithms: In-depth knowledge of mathematics, statistics, and algorithms.
- ML Modules and REST API: Proficient with ML modules and REST API.
- Version Control: Hands-on experience with version control applications (GIT).
- Model Deployment and Monitoring: Experience with model deployment and monitoring.
- Data Processing: Ability to turn unstructured data into useful information (e.g., auto-tagging images, text-to-speech conversions).
- Problem-Solving: Analytically agile with solid problem-solving capabilities.

📌 Mlops Engineer (Pune)
🏢 EXL
📍 Pune