Description

You will build and scale machine learning pipelines and generative AI applications within IoT products.

Responsibilities

• Leverage distributed training systems to build scalable pipelines for model training and deployment.
• Design solutions to optimize distributed training, hyperparameter optimization, inference latency, and system bottlenecks.
• Research, fine-tune, and serve state-of-the-art LLMs for various business use cases.
• Maintain ML model performance, uptime, and scale while ensuring high code quality and monitoring.
• Build deep learning models and algorithms optimized for parallelism on CPUs and GPUs.

Required Skills

• MS or PhD in Computer Science, Software Engineering, Electrical Engineering, or a related field.
• 3+ years of industry experience with Python in a programming-intensive role.
• 2+ years of experience in classification, clustering, optimization, recommendation systems, graph mining, or deep learning.
• 3+ years of experience with distributed computing frameworks such as Spark or the Kubernetes ecosystem.
• 3+ years of experience with ML frameworks including PyTorch, TensorFlow, Keras, HuggingFace Transformers, or Spark MLlib.
• 3+ years of experience with major cloud computing services.
• Experience building and scaling Generative AI applications using Langchain, PGVector, Pinecone, or AzureML.
• Proficiency with libraries such as scikit-learn, spaCy, gensim, or CoreNLP.

Preferred Skills

• Proficiency in PySpark, containerization services, and Azure ML deployment.
• Experience with CI/CD frameworks and designing scalable services using FastAPI.

Key Skills

Education

Bachelor's degree in Computer Science