Description

Build and deploy machine learning systems in production environments.

Responsibilities

• Develop and maintain scalable data pipelines and feature engineering processes.
• Implement supervised and unsupervised learning models, including deep learning architectures (CNNs, RNNs) and LLMs.
• Deploy and monitor ML models using MLOps practices.
• Manage ML workloads using AWS services (SageMaker, Glue, Lambda, EC2, EMR, Athena, Bedrock, Redshift).

Required Skills

• 5+ years of experience in ML/AI development.
• Strong proficiency in Python and SQL.
• Experience with PySpark for large-scale data processing.
• Familiarity with ML frameworks such as TensorFlow and PyTorch.
• Skilled in MLOps tooling, including MLflow, Docker, and FastAPI.
• Proven ability to build data lakehouse architectures.
• Experience with LLM techniques like RAG and fine-tuning.
• Proficiency with core AWS services (SageMaker, Glue, Lambda, EC2, EMR, Athena, Bedrock, Redshift).

Key Skills

Education

Any Graduate