Lead AI/ML & MLOps Engineer executing projects from data foundations to model deployment. Collaborating with sales to drive AI/ML engagements for our clients.
About the role
- Applied ML Engineer working on AI-driven insights at Kaseya. Collaborating with product teams to enhance features with machine learning and data analysis.
Responsibilities
- Explore and analyze data using Python, pandas, and PySpark (or similar tools).
- Use matrix factorization, clustering, dimensionality reduction, and related techniques to understand and prepare data for modeling, and to identify and label latent factors (e.g., user behavior patterns, content/topic clusters, expertise dimensions).
- Create, tune, and productionize ML models for:
- • Categorization / classification
- • Recommendations and similarity
- • Other prediction or ranking tasks that power product features
- Design and implement AI-driven ingest flows that turn unstructured inputs (tickets, emails, forms, messages, logs, etc.) into well-structured data that models and downstream systems can use.
- Build workflows where AI can:
- • Auto-fill or suggest key fields and metadata.
- • Proactively ask users/customers for missing or ambiguous information (e.g., via email or messaging).
- • Surface similar past items or solutions to assist humans in decision-making.
- • Fully handle simple, repetitive “Level 1” style requests end-to-end when safe to do so.
- Work closely with engineers to integrate models and workflows into production systems with proper monitoring, fallbacks, and guardrails.
- Work with multiple product teams to help them identify and scope AI opportunities in their areas.
- Define patterns, templates, and best practices for data ingestion, feature creation, model usage, and evaluation that teams can reuse.
- Serve as a trusted advisor and technical lead:
- • Provide design and architecture guidance on data and ML-heavy features.
- • Join projects to handle the most complex modeling or workflow automation pieces when teams get stuck.
- Mentor and guide junior data/ML engineers and analysts:
- • Conduct code and model reviews.
- • Pair with them on tricky problems.
- • Help them develop good intuitions about metrics, evaluation, and operational reliability.
- Help establish and socialize standards for experimentation, documentation, and responsible AI usage across teams.
Requirements
- 5+ years in data science, ML engineering, or a similar applied role, with a strong record of shipping production data/ML features.
- Strong Python skills and experience with pandas for data analysis.
- Experience with PySpark or other distributed data processing frameworks.
- Solid understanding of ML fundamentals, including:
- • Supervised learning and classification models
- • Matrix factorization / embeddings / latent factor models
- • Feature engineering and model evaluation (offline metrics and online experiments)
- Proficiency with PyTorch (or a similar deep learning framework) and related ML tooling.
- Strong SQL and experience with modern data warehouses / data lakes.
- Comfort working with APIs, microservices, and production integration of ML models, including performance and reliability considerations.
- Experience serving as a technical lead or senior individual contributor across multiple teams or projects.
- Proven ability to translate business problems into data/ML projects, and to clearly explain tradeoffs to non-ML stakeholders.
- Track record of mentoring junior engineers/analysts and improving team practices (e.g., review culture, testing, monitoring).
- Strong communication skills and the ability to drive alignment across product, engineering, and operations.
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