Machine Learning Engineer

Responsibilities

• Design and implement AI product features and maintain existing AI systems
• Train, evaluate, deploy, and monitor ML models
• Design ML pipelines for experiment, model, and feature management
• Implement A/B testing and scalable model inferencing APIs
• Optimize GPU architectures, parallel training, and fine-tune models
• Deploy LLM solutions and ensure DevOps/LLMOps best practices using Kubernetes and Docker
• Build scalable data pipelines and implement state-of-the-art ML techniques like RAG and Transformers

Requirements

• Master’s degree or equivalent experience in Machine Learning
• 5+ years of industry experience in ML, software engineering, and data engineering
• Proficiency in Python, PyTorch, TensorFlow, and Scikit-learn
• Strong programming skills in Python and JavaScript
• Hands-on experience with ML Ops practices
• Experience with LLMs (Hugging Face, GPT, Gemini, Claude, Llama) and LLMOps (Langchain, LlamaIndex, SageMaker)
• Knowledge of databases like MongoDB, PostgreSQL, Pinecone, or ChromaDB
• Experience with Cloud platforms (AWS, Azure) and DevOps tools (Kubernetes, Docker)

Preferred Qualifications

• AWS Professional Solution Architect certification
• AWS Machine Learning Specialty certification
• Azure Solutions Architect Expert certification