Applycup Hiring Solutions is seeking for our client CitiusTech a highly experienced and driven Solution Architect specializing in ML Operations (MLOps) to join our dynamic team. This critical role will be responsible for designing, building, and implementing robust and scalable MLOps solutions that empower our data science teams to effectively deploy and manage machine learning models in production. The ideal candidate possesses a deep understanding of the MLOps landscape, including CI/CD pipelines, model monitoring, and automated retraining strategies. You will collaborate closely with data scientists, engineers, and business stakeholders to translate business requirements into technical solutions, ensuring seamless integration of ML models into our existing infrastructure. A strong background in cloud computing platforms (e.g., AWS, Azure, GCP) is essential, along with experience in containerization technologies like Docker and Kubernetes. You will be a key contributor to establishing best practices for MLOps within the organization, driving innovation and efficiency in our model deployment processes. Excellent communication and collaboration skills are crucial as you will be leading technical discussions, mentoring junior team members, and presenting solutions to both technical and non-technical audiences. The successful candidate will demonstrate a proven track record of delivering impactful MLOps solutions in complex environments, contributing to the overall success of our data-driven initiatives. This role offers a unique opportunity to shape the future of MLOps within a fast-growing company and contribute to cutting-edge projects.

Requirement:

• Extensive experience in designing and implementing MLOps solutions.
• Proficiency in cloud platforms (AWS, Azure, or GCP).
• Strong understanding of containerization technologies (Docker, Kubernetes).
• Excellent communication and collaboration skills.
• Minimum 10 to 15 years of overall experience.
• Job Type: Payroll

Role and responsibility:

• Design and implement robust MLOps pipelines for automated model deployment and management.
• Develop and implement strategies for model monitoring, performance tracking, and automated retraining.
• Collaborate with data scientists and engineers to integrate ML models into production systems.
• Establish best practices for MLOps within the organization, including version control, testing, and deployment processes.
• Provide technical guidance and mentorship to junior team members on MLOps best practices and technologies.