Analytics Engineer; AI & Predictive

Staff Analytics Engineer (AI & Predictive)

The Staff Analytics Engineer (AI & Predictive) is a senior, hands-on individual contributor responsible for designing, building, and operationalizing predictive analytics, traditional machine learning models, agentic AI systems, and Databricks-native data applications that drive real business outcomes. This role operates at the intersection of data science, ML engineering, and full-stack data application development, with a strong focus on production-grade solutions.

This position requires deep expertise in classical ML techniques, agent-based AI workflows, and Databricks application development, along with strong ownership of end-to-end delivery—from data preparation and modeling to deployment, monitoring, and user-facing experiences.

This role requires full-time onsite work in San Diego, CA (5 days per week).

** This position is not eligible for Qualcomm immigration sponsorship.**

• Design, develop, and deploy traditional machine learning models, including regression, classification, clustering, time-series forecasting, and anomaly detection.
• Perform feature engineering, model selection, training, validation, and performance tuning on large-scale enterprise datasets.
• Apply sound statistical and ML best practices to ensure model robustness, explainability, and business relevance.
• Design and implement agentic AI workflows, where autonomous or semi-autonomous agents orchestrate data access, ML inference, decision logic, and actions.
• Build multi-step agent pipelines that combine rules, ML models, and reasoning components to solve complex business problems.
• Integrate agentic systems with enterprise data, ML models, and applications to enable intelligent automation and decision support.
• Design and develop Databricks-native applications, including notebook-based apps, interactive dashboards, and parameterized data/ML workflows.
• Build data and ML services/APIs leveraging Databricks, Python, and Lakehouse capabilities.
• Partner with analytics, BI, and application teams to embed ML insights, predictions, and agent outputs directly into Databricks apps and business workflows.
• Operationalize ML models and agentic workflows into production pipelines, ensuring scalability, reliability, and monitoring.
• Collaborate with data engineering teams to leverage curated Lakehouse data, feature stores, and governed datasets.
• Implement model monitoring, drift detection, and retraining strategies to maintain long-term model effectiveness.
• Develop end-to-end solutions that span data ingestion, modeling, ML inference, agent execution, and user-facing applications.
• Translate business and analytical requirements into scalable, maintainable ML-powered data products.
• Enable downstream consumption through Databricks apps, dashboards, APIs, and integrated enterprise applications.
• Own production ML models, agentic systems, and Databricks applications, including monitoring, troubleshooting, and root-cause analysis.
• Implement logging, alerting, and observability for models, agents, and applications.
• Drive continuous improvements in model accuracy, system reliability, and user experience.
• Serve as a technical authority in traditional ML, agentic AI, and Databricks application patterns.
• Influence architectural decisions, best practices, and technical standards across teams.
• Mentor peers and raise the bar on ML rigor, engineering quality, and production readiness.

Required Skills & Experience

• 5+ years of hands-on experience in data science, applied machine learning, or ML engineering, with ownership of production systems.
• Strong proficiency in Python for ML development, data processing, and application logic.
• Deep experience with traditional ML techniques (e.g., regression, classification, clustering, time series).
• Proven experience building and deploying ML models in production environments.
• Hands-on experience with Databricks, including Databricks application development (notebooks, workflows, dashboards, ML pipelines).
• Strong understanding of feature engineering, model evaluation, and explainability.
• Experience collaborating with data engineering, BI, and application teams.

Preferred…