Description

You will architect and implement end-to-end AI/ML solutions, from model design to scalable deployment.

This role is on-site.

Responsibilities

• Design and deploy supervised and unsupervised models, including neural networks (CNNs, RNNs, LSTMs) and boosting methods.
• Develop Generative AI solutions using LLMs and foundation models, applying prompt engineering and fine-tuning techniques like LoRA and PEFT.
• Manage the full model lifecycle by containerizing deployments with Docker and Kubernetes and building scalable endpoints using FastAPI.
• Drive adoption of the Databricks platform to automate model development and create reusable AI solutions.
• Write efficient, maintainable Python code, enforcing Git version control and testing best practices.

Required Skills

• 5+ years of experience designing and deploying end-to-end AI/ML systems.
• Advanced proficiency in Python for machine learning and data manipulation.
• Deep expertise in Generative AI, including LLMs, foundation models, and fine-tuning.
• Proven experience with MLOps principles, specifically Docker and Kubernetes.
• Expertise in cloud platform deployment and infrastructure management on AWS and/or Azure.
• Proficiency with core ML libraries: PyTorch, scikit-learn, pandas, and NumPy.
• Experience building user-facing prototypes using Streamlit and standard web technologies (HTML, CSS, JavaScript).
• Strong functional knowledge of Databricks for AI/ML acceleration.

Preferred Skills

• Bachelor's degree in Computer Science, Engineering, or a related field.
• Experience with time-series forecasting and regression models.

Key Skills

Education

Bachelor's degree