Manager

We are inviting applications for the role of Manager-ML Ops Engineer

Responsibilities
Design, develop, and implement ML/LLM pipelines for generative AI models, encompassing data ingestion, pre-processing, training, deployment, and monitoring.
MLOps Support and Maintenance on ML Platforms Dataiku/Sagemaker
Apply understanding of Dataiku Govern functionalities, including item and artifact management, workflow control, and sign-off processes.
Using best practices for data governance and model accountability within the MLOps lifecycle.
Automate ML tasks across the model lifecycle, leveraging tools like GitOps, CI/CD pipelines, and containerization technologies (e.g., Docker, Kubernetes).
Implement version control, CI/CD pipelines, and containerization techniques to streamline ML and LLM workflows.
Design and implement monitoring and alerting systems to track model performance, data drift, and other key metrics.
Conduct ground truth analysis to evaluate the accuracy and effectiveness of LLM outputs compared to known, correct data.
Work closely with infrastructure and DevOps teams to provision and manage resources for ML and LLM development and deployment.
Develop and maintain robust monitoring and alerting systems for generative AI models in production, ensuring proactive identification and resolution of issues.
Collaborate with the Generative AI Full Stack Architect and other engineers to optimize model performance and resource utilization.
Manage and maintain cloud infrastructure (e.g., AWS, Azure) for ML workloads, ensuring cost-efficiency and scalability.
Stay up to date on the latest advancements in MLOps and incorporate them into our platform and processes.
Communicate effectively with technical and non-technical stakeholders about the health and performance of generative AI models.

Qualifications we seek in you
Minimum Qualifications
Bachelor s degree in computer science, Data Science, Engineering, or a related field, or equivalent experience.
Experience in MLOps or related areas, such as DevOps, data engineering, or ML infrastructure.
Knowledge of best practices for data governance and model accountability within the MLOps lifecycle.
Covered these Dataiku Certifications ML Practitioner Certificate, Advanced Designer Certificate and MLOps Practitioner Certificate.

Preferred Qualifications
Proven experience in ML Ops, LLM Ops, or related roles, with hands-on experience deploying and managing machine learning and large language model pipelines Expertise in cloud platforms (e.g., AWS, Azure) for ML workloads.
Strong understanding of CI/CD principles and containerization technologies like Docker and Kubernetes.
Familiarity with monitoring and alerting tools for ML systems (e.g., Prometheus, Grafana).
Excellent communication, collaboration, and problem-solving skills.
Ability to work independently and as part of a team.
Passion for Generative AI and its potential to revolutionize various industries.

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