ROLE Summary

We are seeking a highly skilled ML Engineer to design, implement and operate scalable, secure, and production grade machine learnings platforms on Databricks. The role focused on enabling reliable model development, deployment, monitoring, and lifecycle management across large-scale AI workloads.

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Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability Solutions, and Industrial Automation business segments – powered by our Honeywell Forge software – that help make the world smarter, safer and more sustainable.

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As a Software Engr II here at Honeywell, you will design, develop, and maintain software applications, collaborate with cross-functional teams, and ensure timely project completion. Impact innovative solutions driving efficiency and productivity.You will report directly to our [Title] and you’ll work out of our [City, State] location on a [Hybrid, On-site, Remote] work schedule.

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Must-Have Skills

• Strong experience in MLOps, ML Engineering.
• Hands-on expertise with Azure Databricks for ML training and execution.
• Solid experience with MLflow (experiment tracking, model registry, artifact management).
• Strong understanding of Unity Catalog for data, feature, and model governance.
• Experience deploying and managing model serving / inference endpoints.
• Experience with containerization (Docker) and ML deployment workflows.
• Knowledge of model monitoring, performance tracking, and data / concept drift.
• Strong understanding of Databricks architecture.
• Proficient in programming with Python, SQL, and PySpark.

Good-to-Have Skills

• Experience with Databricks Feature Store or equivalent feature management platforms.
• Experience with governance, compliance, and auditability in regulated environments.
• Familiarity with cost optimization strategies for large-scale ML workloads.
• Knowledge of blue/green, canary, or Champion Challenger deployments for ML models.

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Key Responsibilities

• Design, implement, and operate a scalable, production-grade machine learning platform on Databricks.
• Enable end-to-end ML lifecycle management including experimentation, model versioning, deployment, and monitoring.
• Build and maintain standardized automation frameworks for ML workflows using CI/CD best practices.
• Implement governed experiment tracking, model registry, and artifact management to ensure reproducibility and auditability.
• Deploy and operate production model inference solutions supporting real-time and batch workloads.
• Establish monitoring and observability for deployed models, including performance, data quality, and drift indicators.
• Enable shared and governed feature management capabilities to support reuse across ML use cases.
• Apply centralized governance, access control, and lineage for data, features, and models.
• Optimize ML workloads for scalability, cost efficiency, reliability, and security.
• Provide operational support, maintenance, and continuous improvement for production ML systems.