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.

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 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 6 years of hands-on experience in MLOps, deploying and managing machine learning models in production environments, preferably in cloud-based environments.

Hi,

I came across your profile and was impressed by your experience in MLOps and related technologies. We’re hiring for an MLOps Engineer role, and your background seems like a great fit. Would you be open to discussing this opportunity further? I’d be happy to share more details.

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.

Hi,

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.

Hi,