Key Responsibilities
- Design, develop, and deploy machine learning models that power autonomous functionality within our cloud- based operations management platform.
- Conduct on-site visits to observe real-world system environments and evaluate model performance in action.
- Collaborate closely with cross-functional teams, including product management and engineering, to define requirements and integrate ML solutions into production systems.
- Conduct exploratory data analysis for model development and optimization.
- Continuously evaluate model performance and implement improvements to enhance accuracy, reliability, and scalability.
- Stay current with advancements in machine learning research and tools, applying relevant innovations to real- world challenges.
- Build and maintain robust data pipelines and workflows to enable training and deployment of ML models in a cloud-native environment.
Required Qualifications
- Masters in Computer Science, Data Science, Artificial Intelligence, or a related field, OR equivalent experience.
- PhD is a plus.
- Demonstrated proficiency in Python and relevant ML libraries (e.g., PyTorch, Scikit-learn).
- Strong understanding of machine learning algorithms, statistical modeling, and data structures.
- Solid knowledge of data engineering concepts and experience working with large-scale, real world datasets.
- Proven ability to turn complex problems into practical, deployable solutions.
- Excellent communication and collaboration skills, with the ability to explain technical concepts to non-technical stakeholders.
Preferred
- Experience deploying machine learning models in cloud environments (e.g., AWS, Azure, or Google Cloud).
- Experience with developing LLMs and agentic AI.
- Familiarity with tabular and sparse data. Exposure to time-series data or anomaly detection use cases.
- Familiarity with online learning and other ML techniques.
- Contributions to open-source projects, published research in machine learning or applied data science, or relevant personal projects.
- Familiarity with MLOps best practices, including model monitoring, drift detection, and retraining pipelines.
- Prior experience working on B2B SaaS platforms operational technology systems.