Machine Learning Engineer

About The Role The role drives the development and production deployment of machine learning models that power core product experiences. As a Machine Learning Engineer on this team, the focus is on bridging the gap between theoretical ML models and highly scalable, low-latency production services. This position is responsible for designing, training, and deploying deep learning and NLP architectures, with a strong emphasis on large language model integration, fine-tuning, and robust evaluation systems. Key Responsibilities Design and implement end-to-end ML pipelines including data ingestion, feature extraction, model training, and real-time inference. Deploy, monitor, and maintain ML models in production environments using Docker, Kubernetes, and cloud infrastructure like AWS or GCP. Optimize model architectures and inference pipelines to meet strict latency and throughput requirements for real-time serving. Build and scale retrieval-augmented generation (RAG) pipelines and integrate LLMs into production workflows using frameworks like LangChain or LlamaIndex. Establish automated monitoring and alerting systems to detect model drift, data quality issues, and performance degradation in real-time. Collaborate with backend engineers to integrate model outputs into user-facing applications via REST and gRPC APIs. What We Are Looking For 3–6 years of professional experience as a Machine Learning Engineer or software engineer building production-grade ML applications. Strong programming skills in Python and deep familiarity with ML frameworks such as PyTorch, TensorFlow, or JAX. Proven experience deploying ML models to cloud environments (AWS SageMaker, Vertex AI) and managing containerized applications with Kubernetes. Solid understanding of vector databases (e.g., Pinecone, Milvus, Qdrant) and semantic search architectures. BS, MS, or PhD in Computer Science, Data Science, or a related quantitative field. Bonus: Experience with parameter-efficient fine-tuning (PEFT, LoRA), Triton Inference Server, or custom ML compiler toolchains like TensorRT. Show more Show less