Description

You will build and maintain machine learning systems, from data pipelines to model deployment, with a focus on LLM applications.

This role is on-site.

Responsibilities

• Develop and optimize ML pipelines using Python, including model training and API development.
• Implement LLM solutions using prompt engineering, chaining, and agent frameworks like LangChain or LangGraph.
• Build and maintain robust data pipelines to support ML workflows and experiment tracking with MLflow or Weights & Biases.
• Apply strong software engineering practices, including Git, testing, and clean code documentation.
• Collaborate on production ML deployment and RAG system integration.

Required Skills

• 3–6 years of experience in ML, AI engineering, or applied roles.
• Strong Python proficiency for pipelines, model training, and APIs.
• Hands-on experience with PyTorch, TensorFlow, or scikit-learn.
• Practical experience with LLM frameworks (LangChain, LangGraph) and RAG systems.
• Experience with experiment tracking tools such as MLflow or Weights & Biases.
• Strong software engineering fundamentals: Git, testing, documentation.

Preferred Skills

• Experience with hardware performance data or systems-level modeling.
• Familiarity with dataset versioning (DVC) and containerization (Docker).
• Experience building internal developer tools.

Bachelor's, Master's, or PhD in Computer Science, Machine Learning, or related field — or equivalent hands-on experience.

Key Skills

Education

Any Graduate