Lead Assistant Manager

Job Responsibilities

• 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.
  
  

• 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 strong problem-solving capabilities.
• Learning Agility: Quick to learn new concepts and eager to explore and build new features.
  
  

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
• Experience: Minimum of 4 years of hands-on experience in MLOps, deploying and managing machine learning models in production environments, preferably in cloud-based environments.