Overview

Role & responsibilities

Responsibilities

• Advanced proficiency in Python with expertise in data science libraries (NumPy, Pandas, scikit-learn) and deep learning frameworks (PyTorch, TensorFlow)
• Extensive experience with LLM frameworks (Hugging Face Transformers, LangChain) and prompt engineering techniques

Qualifications

• Version control and experiment tracking using Git and MLflow
• Software Engineering & Development: Advanced proficiency in Python, familiarity with Go or Rust, expertise in microservices, test-driven development, and concurrency processing.
• DevOps & Infrastructure: Experience with Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes) with Helm and service mesh implementations.
• LLM Infrastructure & Deployment: Proficiency in LLM serving platforms such as vLLM and FastAPI, model quantization techniques, and vector database management.
• MLOps & Deployment: Utilization of containerization strategies for ML workloads, experience with model serving tools like TorchServe or TF Serving, and automated model retraining.
• Cloud & Infrastructure: Strong grasp of advanced cloud services (AWS, GCP, Azure) and network security for ML systems.
• LLM Project Experience: Expertise in developing chatbots, recommendation systems, translation services, and optimizing LLMs for performance and security.
• General Skills: Python, SQL, knowledge of machine learning frameworks (Hugging Face, TensorFlow, PyTorch), and experience with cloud platforms like AWS or GCP.
• Strong mathematical foundation in statistics, probability, linear algebra, and optimization
• Deep understanding of ML and LLM development lifecycle, including fine-tuning and evaluation
• Expertise in feature engineering, embedding optimization, and dimensionality reduction
• Advanced knowledge of A/B testing, experimental design, and statistical hypothesis testing
• Proficiency in LLM optimization techniques including quantization and knowledge distillation
• Understanding of MLOps practices for model deployment and monitoring
• Strong analytical thinking with ability to solve complex ML challenges
• Excellent communication skills for presenting technical findings to diverse audiences
• Project management skills for coordinating ML experiments and deployments
• Strong collaboration abilities for working with cross-functional teams
• Dedication to staying current with latest ML research and best practices
• Ability to mentor and share knowledge with team members