Company | Honeywell |
Job title | Advanced ML Engineer |
Job location | Hyderabad, Telangana, India |
Type | Full Time |
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