Machine Learning Engineer

About The Role

The role drives the development and scaling of core machine learning systems, bridging the gap between applied research and high-performance production engineering. The team focuses on deploying robust predictive models and deep learning architectures that power real-time personalization, recommendation systems, and search capabilities.

Working within a collaborative team of software developers and data scientists, this role is critical to building scalable MLOps infrastructure. Success in this position means delivering low-latency inference services while maintaining high model accuracy, direct scalability, and rigorous automated validation.

Key Responsibilities

• Design, train, and deploy production-grade machine learning models using frameworks such as PyTorch, TensorFlow, and XGBoost
• Build and maintain robust feature stores and scalable data pipelines using PySpark, SQL, and orchestration tools like Airflow
• Deploy real-time inference endpoints and batch prediction pipelines using containerization tools such as Docker, Kubernetes, and Triton Inference Server
• Implement comprehensive MLOps pipelines using MLflow, Weights & Biases, or AWS SageMaker for model registry, lineage tracking, and performance monitoring
• Establish automated testing and validation workflows for models to catch data drift, concept drift, and performance regressions before deployment
• Optimize deep learning models for latency and throughput using techniques like quantization, pruning, and ONNX runtime integration

What We Are Looking For

• 3-7 years of professional experience as a Machine Learning Engineer, Software Engineer (ML), or Data Scientist in a production environment
• Strong software engineering fundamentals in Python, with deep knowledge of algorithms, data structures, and object-oriented design
• Demonstrated experience deploying and monitoring ML models in a cloud environment, preferably AWS or GCP
• Solid theoretical understanding of machine learning algorithms, statistical modeling, and deep learning architectures
• Experience with SQL and distributed data processing technologies such as Spark, Flink, or Hadoop
• Bonus: Experience with LLM fine-tuning, retrieval-augmented generation (RAG) pipelines, or vector databases such as Milvus or Pinecone