Machine Learning Engineer

Role overview

You will design, train, and scale production-grade machine learning systems that bridge applied research and high-performance engineering. The work centers on low-latency inference and robust model validation to power real-time personalization, recommendation, and search capabilities.

Responsibilities

• Design, train, and deploy ML models for production using PyTorch, TensorFlow, and XGBoost
• Build and maintain feature stores and scalable data pipelines using PySpark, SQL, and Airflow
• Deploy real-time inference endpoints and batch prediction pipelines using Docker, Kubernetes, and Triton Inference Server
• Implement MLOps pipelines for model registry, lineage, and monitoring using MLflow, Weights & Biases, or AWS SageMaker
• Create automated testing/validation to detect issues like data drift, concept drift, and performance regressions
• Optimize deep learning models for latency/throughput using quantization, pruning, and ONNX Runtime integration

Requirements

• 3–7 years professional experience as an ML Engineer / Software Engineer (ML) / Data Scientist in production
• Strong Python software engineering fundamentals (algorithms, data structures, OOP)
• Experience deploying and monitoring ML models in the cloud, preferably AWS or GCP
• Solid theoretical understanding of ML algorithms, statistical modeling, and deep learning architectures
• Experience with SQL and distributed data processing (e.g., Spark, Flink, Hadoop)

Bonus

• Experience with LLM fine-tuning
• RAG (retrieval-augmented generation) pipelines
• Vector databases such as Milvus or Pinecone