Description

You will build and scale machine learning pipelines for model training, deployment, and inference optimization.

This role is remote.

Responsibilities

• Design and implement scalable ML pipelines using distributed training systems.
• Optimize distributed training, hyperparameter optimization, and inference latency.
• Fine-tune and serve state-of-the-art LLM models for various use cases.
• Build deep learning models optimized for parallelism on CPUs and GPUs.
• Maintain model performance, uptime, and high code quality.

Required Skills

• 3+ years of industry experience with Python in a programming-intensive role.
• 3+ years with distributed computing frameworks such as Spark or Kubernetes.
• 3+ years with ML frameworks including Keras, TensorFlow, PyTorch, or HuggingFace Transformers.
• 3+ years of experience with major cloud computing services.
• 2+ years in classification, clustering, optimization, recommendation systems, 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