Machine learning specialist

5 days ago

Noida, India EXL Full time
MLOps Engineer
Experience Required: 4+ years of relevant experience
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 Dev Ops 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 Dev Ops 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 No SQL 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.
Qualifications:
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

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