As an Applied ML Engineer at Kaseya, your primary responsibility will be to partner with multiple product teams to extract insights from data and build AI-powered features and automated workflows across the product suite. You will play a crucial role in enabling product teams by teaching, coaching, and guiding them on data and ML best practices. Additionally, you will lead by example by conducting complex data analysis and ML modeling, architecture, and implementation work to accelerate teams while mentoring more junior data/ML professionals. **Key Responsibilities:**
Explore and analyze data using Python, pandas, and PySpark (or similar tools)
Utilize matrix factorization, clustering, dimensionality reduction, and related techniques to prepare data for modeling and identify latent factors
Create, tune, and productionize ML models for categorization/classification, recommendations, and other prediction tasks
Design and implement AI-driven workflows that convert unstructured inputs into well-structured data
Collaborate with engineers to integrate models and workflows into production systems with proper monitoring and fallbacks
Work with multiple product teams to identify AI opportunities and define best practices for data ingestion, feature creation, and model usage
Serve as a trusted advisor and technical lead by providing design and architecture guidance on data and ML-heavy features
**Qualifications Required:**
5+ years of experience in data science, ML engineering, or a similar applied role with a track record of shipping production data/ML features
Strong Python skills, experience with pandas, and proficiency in PySpark or other distributed data processing frameworks
Solid understanding of ML fundamentals such as supervised learning, classification models, and feature engineering
Proficiency with PyTorch or a similar deep learning framework, strong SQL skills, and experience with modern data warehouses/data lakes
Comfort working with APIs, microservices, and production integration of ML models
Experience serving as a technical lead across multiple teams or projects and mentoring junior engineers/analysts
Strong communication skills and the ability to drive alignment across product, engineering, and operations
In addition to these qualifications, experience with LLMs, language-centric workflows, MLOps tools, building agent-assist features, and prior experience in a platform/enablement role supporting multiple product teams with shared data and ML capabilities would be advantageous. Please note that Kaseya provides equal employment opportunity to all employees and applicants without regard to various characteristics protected by applicable law. As an Applied ML Engineer at Kaseya, your primary responsibility will be to partner with multiple product teams to extract insights from data and build AI-powered features and automated workflows across the product suite. You will play a crucial role in enabling product teams by teaching, coaching, and guiding them on data and ML best practices. Additionally, you will lead by example by conducting complex data analysis and ML modeling, architecture, and implementation work to accelerate teams while mentoring more junior data/ML professionals. **Key Responsibilities:**
Explore and analyze data using Python, pandas, and PySpark (or similar tools)
Utilize matrix factorization, clustering, dimensionality reduction, and related techniques to prepare data for modeling and identify latent factors
Create, tune, and productionize ML models for categorization/classification, recommendations, and other prediction tasks
Design and implement AI-driven workflows that convert unstructured inputs into well-structured data
Collaborate with engineers to integrate models and workflows into production systems with proper monitoring and fallbacks
Work with multiple product teams to identify AI opportunities and define best practices for data ingestion, feature creation, and model usage
Serve as a trusted advisor and technical lead by providing design and architecture guidance on data and ML-heavy features
**Qualifications Required:**
5+ years of experience in data science, ML engineering, or a similar applied role with a track record of shipping production data/ML features
Strong Python skills, experience with pandas, and proficiency in PySpark or other distributed data processing frameworks
Solid understanding of ML fundamentals such as supervised learning, classification models, and feature engineering
Proficiency with PyTorch or a similar deep learning framework, strong SQL skills, and experience with modern data warehouses/data lakes
Comfort working with APIs, microservices, and production integration of ML models
Experience serving as a technical lead across multiple teams or projects and mentoring junior engineers/analysts
Strong communication skills and the ability to drive alignment across product, engineering, and o