Description

You will build and maintain machine learning platform infrastructure and model deployment pipelines.

Responsibilities

• Develop and deploy model pipelines using AWS SageMaker.
• Implement MLOps best practices for model development, deployment, and monitoring.
• Apply statistical learning algorithms and transformer architectures to solve business problems.
• Build and manage vectorized databases for data storage and retrieval.
• Implement LLM architecture patterns including RAG and FLARE.

Required Skills

• 3+ years of professional experience in Machine Learning and Data Science.
• Expert-level Python programming skills.
• Strong experience with AWS technologies including S3, EC2, and RDS.
• Deep understanding of neural networks, transformer algorithms, and large language models.
• Experience with prompt engineering, fine-tuning, and open-source LLM foundation models.
• Knowledge of LLM performance evaluation using NLP and LLM-assisted metrics.
• Proficiency with statistical learning algorithms like GLM, XGBoost, and Random Forest.
• Bachelor’s or Master’s degree in Computer Science or equivalent professional competency.

Preferred Skills

• Data engineering experience with SQL, Spark, or AWS Glue.
• Experience with Docker, Kubernetes, or creating API services.
• Infrastructure and CI/CD experience using TeamCity and Terraform.

Key Skills

Education

Bachelor's degree in Computer Science