Description

Build and scale machine learning 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 to solve business problems.
• Optimize LLM performance through prompt engineering, fine-tuning, and architecture patterns like RAG and FLARE.
• Manage data storage and retrieval using vectorized databases.

Required Skills

• 3+ years of professional experience in Machine Learning and Data Science.
• Expert-level Python programming skills.
• Deep understanding of neural networks and transformer algorithms for LLMs and chat models.
• Proficiency with AWS cloud technologies including S3, EC2, and RDS.
• Experience with statistical learning algorithms such as GLM, XGBoost, and Random Forest.
• Familiarity with open source LLM foundation models.
• Knowledge of LLM performance evaluation using NLP and LLM-assisted metrics.
• 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, TeamCity, or Terraform for CI/CD and containerization.
• Experience building API services or working in the insurance, banking, or finance industries.

Key Skills

Education

Bachelor's degree in Computer Science