AI/ML Engineer

Job Description:

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.1.    Background or experience in building and scaling Generative AI Applications, specifically around frameworks like Langchain, PGVector, Pinecone, AzureML
2.    Industry experience with popular ml frameworks such as Spark MLlib, Keras, Tensorflow, PyTorch, HuggingFace Transformers and libraries (like scikit-learn, spacy, gensim, CoreNLP etc.).
Experience in designing scalable services controller architecture using FastAPI.•    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