Advanced ML Engineer, Honeywell

Responsibilities:

Collaborate with colleagues across multiple function groups (Data Science, Data Engineering, domain experts) on unique challenges across different business units of Honeywell.
• Develop complicated, scalable and robust analytics solutions to solve business problems.
• Leverage distributed training systems to build scalable machine learning pipelines for model training and deployments in IT/OT space.
• Design and implement solutions to optimize distributed training execution in terms of model hyperparameter optimization, model training/inference latency and system-level bottlenecks.
• Ensure ML Model performance, uptime, and scale, maintaining high standards of code quality and thoughtful design quality and monitoring.
• Optimize integration between popular machine learning libraries and cloud ML and data processing frameworks.

Requirements & Skills:

• Bachelor of Technology in Computer Science & Engineering
• 6-8 years of strong experience with the following machine learning topics: classification, clustering, optimization, deep learning, and NLP with Python in a programming-intensive role.
• 6-8 years of strong experience in Python / PySpark coding
• 4-6 years of industry experience with popular ML frameworks such as Keras, Tensorflow, PyTorch, HuggingFace Transformers, and libraries (like sci-kit-learn, etc.).
• 4-6 years of strong experience in Azure Databricks/Azure AWS and end-to-end MLOps Architecture and MLflows.
• 3-5 years of industry experience with distributed computing frameworks such as Spark, Kubernetes ecosystem, etc.
• 3-5 years of experience with CI/CD Dev Ops process
• Proficient in Snowflake, Database Concepts
• Proficient in Azure Data Factory
• Exposure to the Agile Methodology process
• Proficient in containerization services
• Proficient in MLOps Stacks to deploy models like Azure ML, Databricks, etc.
• Proficient in Databricks Unity Catalog,
• Proficient in storage solutions such as Azure ADLS, Postgres SQL, Delta Table, etc