Machine Learning Engineer

About The Role The role owns the end-to-end design, scaling, and operationalization of machine learning and GenAI systems deployed in high-throughput production environments. The team works at the intersection of applied research and scalable infrastructure, solving complex challenges in model latency, accuracy, and reliability. Key Responsibilities Design, train, and deploy production-grade machine learning models and LLM-based systems using Python and PyTorch Build robust data pipelines for feature extraction, data cleaning, and vector embedding generation using modern data stacks Implement Retrieval-Augmented Generation (RAG) pipelines and orchestrate LLM workflows using frameworks like LangChain and LlamaIndex Monitor deployed models for data drift, concept drift, and performance degradation using automated logging and observability tools Optimize model inference latency, throughput, and compute costs through quantization, pruning, and hardware acceleration techniques Write clean, testable, and well-documented code, participating actively in peer code reviews and architectural discussions What We Are Looking For 3-6 years of professional experience in software engineering and machine learning engineering with production deployments Strong proficiency in Python and hands-on experience with deep learning frameworks such as PyTorch or TensorFlow Demonstrated experience with cloud-based ML infrastructure including AWS, GCP, or Azure Solid understanding of vector databases, embedding generation, and semantic search architectures Degree in Computer Science, Statistics, Mathematics, or a related technical field Bonus: Experience fine-tuning open-source LLMs using LoRA/QLoRA and contributions to open-source ML projects Show more Show less