About the role
Data engineering: PySpark, SQL, batch and streaming approaches, Azure technologies, Databricks, data modelling
Data science: AI/ML model development and validation, AutoML, pandas, pyspark, Timeseries analysis, MLOps processes, etc.
- Collaborate with colleagues across multiple teams (Data Science and Data Engineering) on unique machine learning system challenges at scale.
- Leverage distributed training systems to build scalable machine learning pipelines for model training and deployments in IT/OT Products space.
- Design and implement solutions to optimize distributed training execution in terms of model hyperparameter optimization, model training/inference latency and system-level bottlenecks.
- Research and impalement state of the art LLM models for different business use cases including finetuning and serving the LLMs.
- 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.
- Build Deep Learning models and algorithms with optimal parallelism and performance on CPUs/ GPUs.
Your background and who you are:
- MS or Ph.D. in Computer Science, Software Engineering, Electrical Engineering, or related fields.
- 3+ years of industry experience with Python in a programming intensive role.
- 2+ years of experience with one or more of the following machine learning topics: classification, clustering, optimization, recommendation system, graph mining, deep learning.
- 3+ years of industry experience with distributed computing frameworks such as Spark, Kubernetes ecosystem, etc.
- 3+ years of industry experience with popular ml frameworks such as Spark MLlib, Keras, Tensorflow, PyTorch, HuggingFace Transformers and libraries (like scikit-learn, spacy, gensim, CoreNLP etc.).
- 3+ years of industry experience with major cloud computing services.
- Background or experience in building and scaling Generative AI Applications, specifically around frameworks like Langchain, PGVector, Pinecone, AzureML.
- Prior experience in building data products and established a track record of innovation would be a big plus.
- An effective communicator – you shall be an ambassador of Honeywell’s Machine Learning engineering at external forums and have the ability to explain technical concepts to a non-technical audience.
Preferred Qualifications:
- Proficient Python/PySpark coding experience
- Proficient in containerization services
- Proficient in Azure ML to deploy the models
- Experience with working in CICD framework
- Motivation to make downstream modelers’ work smoother