Description

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 Responsibilities: Mandatory Skill: Background or experience in building and scaling Generative AI Applications, specifically around frameworks like Langchain, PGVector, Pinecone, AzureML 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. 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.

Role and Responsibilities

• 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 Responsibilities: Mandatory Skill: Background or experience in building and scaling Generative AI Applications, specifically around frameworks like Langchain, PGVector, Pinecone, AzureML 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. 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.