Description

You will design, deploy, and monitor machine learning models and large language model architectures.

This role is on-site.

Responsibilities

• Develop model pipelines using AWS SageMaker to solve business problems.
• Implement and fine-tune LLM foundation models using prompt engineering and fine-tuning techniques.
• Build and manage vectorized databases for efficient data storage and retrieval.
• Deploy containerized applications and manage infrastructure via CI/CD pipelines.
• Monitor model performance using NLP and LLM-assisted metrics.

Required Skills

• 5+ 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.
• Practical experience with LLM architecture patterns including RAG and FLARE.
• Proficiency with statistical learning algorithms such as GLM, XGBoost, and Random Forest.
• Strong knowledge of AWS services including S3, EC2, RDS, and SageMaker.
• Application of MLOps best practices for model development, deployment, and monitoring.
• Bachelor'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, and Terraform.
• Experience building API services or working in insurance, banking, or finance.

Key Skills

Education

Bachelor's degree in Computer Science