MLOps Manager

As the MLOps Lead, you will be responsible for leading the deployment and operationalization of AI/ML solutions. You will collaborate with data scientists, engineers, and IT teams to ensure that machine learning models are efficiently deployed, monitored, and maintained. Your role will be crucial in bridging the gap between data science and production, ensuring that our AI initiatives are scalable, reliable, and deliver business value.

Responsibilities:
• Lead the design, implementation, and management of MLOps pipelines to deploy machine learning models into production.

• Work closely with data scientists to understand model requirements and ensure smooth integration and deployment.
• Develop and maintain infrastructure for model training, validation, deployment, and monitoring.
• Implement best practices for CI/CD pipelines in the context of AI/ML model development and deployment.
• Ensure scalability, reliability, and performance of deployed models through monitoring and continuous optimization.
• Leverage containerization and orchestration tools (e.g., Docker, Kubernetes) to manage model deployment environments.
• Collaborate with IT and DevOps teams to ensure seamless integration of AI/ML solutions with existing systems.
• Implement and enforce security best practices for AI/ML models and data pipelines.
• Stay updated with the latest advancements in MLOps tools, frameworks, and methodologies.
• Provide technical leadership and mentorship to junior team members.

Requirements:
• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.

• Proven experience (8+ years) in MLOps, DevOps, or a related field, with a focus on deploying machine learning models.
• Strong understanding of machine learning, deep learning, NLP, and generative AI techniques.
• Proficiency with MLOps tools and frameworks such as MLflow, Kubeflow, TensorFlow Extended (TFX), or similar.
• Experience with CI/CD tools such as Jenkins, GitLab CI, or CircleCI.
• Proficiency in programming languages such as Python and familiarity with ML/DL frameworks like TensorFlow, PyTorch, and scikit-learn.
• Experience with cloud platforms (AWS, GCP, Azure) and their AI/ML services.
• Knowledge of containerization and orchestration tools (Docker, Kubernetes).
• Strong understanding of version control systems (e.g., Git) and collaborative development workflows.
• Excellent problem-solving skills and the ability to design robust, scalable MLOps solutions.
• Strong communication skills, with the ability to collaborate effectively with cross-functional teams.
• Experience in leading and mentoring technical teams.

Preferred Qualifications:

• Experience with big data technologies and tools such as Hadoop, Spark, and Kafka.
• Familiarity with data visualization tools such as Tableau, Power BI, or similar.