Machine Learning Engineer

Role Overview

Own the end-to-end design, scaling, and operationalization of machine learning and GenAI systems in high-throughput production. You’ll work at the intersection of applied research and scalable infrastructure to improve latency, accuracy, and reliability.

Responsibilities

• Design, train, and deploy production-grade ML models and LLM-based systems using Python and PyTorch
• Build robust data pipelines for feature extraction, data cleaning, and vector embedding generation
• Implement Retrieval-Augmented Generation (RAG) pipelines and orchestrate LLM workflows using LangChain and LlamaIndex
• Monitor deployed models for data drift, concept drift, and performance degradation with automated logging/observability
• Optimize inference latency, throughput, and compute costs using techniques like quantization and pruning plus hardware acceleration
• Write clean, testable, well-documented code and participate in peer code reviews and architecture discussions

Requirements

• 3–6 years of professional experience in software engineering and ML engineering with production deployments
• Strong Python skills and hands-on deep learning experience (PyTorch or TensorFlow)
• Experience with cloud ML infrastructure on AWS, GCP, or Azure
• Solid understanding of vector databases, embedding generation, and semantic search architectures
• Degree in Computer Science, Statistics, Mathematics, or related technical field

Bonus

• Experience fine-tuning open-source LLMs using LoRA/QLoRA
• Contributions to open-source ML projects