Description

You will build and scale machine learning pipelines for model training and deployment within the ITOT Products space.

Responsibilities

• Leverage distributed training systems to build scalable ML pipelines for training and deployment.
• Design and implement solutions to optimize distributed training, hyperparameter optimization, inference latency, and system bottlenecks.
• Research and implement state-of-the-art LLM models, including fine-tuning and serving for various 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

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

Preferred Skills

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

Key Skills

Education

Any Graduate