Description

Lead the design, scaling, and deployment of production-grade machine learning solutions and infrastructure.

Responsibilities

• Build and manage end-to-end MLOps pipelines for data collection, model training, validation, and monitoring.
• Develop robust APIs, microservices, and common feature stores to integrate models into production systems.
• Design scalable ML infrastructure and manage compute and storage resources for training and inference.
• Implement model compression, quantization, and distributed training techniques to optimize performance.
• Guide machine learning engineers and align R&D roadmaps with technical and business objectives.

Required Skills

• 12+ years of experience as a Machine Learning Engineer or Architect with deployed batch, streaming, and real-time models.
• Proficiency in Python for model development and data manipulation.
• Experience with Java or Scala for building production systems and microservices.
• Expertise in MLOps tools such as MLflow, Kubeflow, and Airflow.
• Hands-on experience with cloud platforms (AWS, Google Cloud, or Azure) and containerization (Docker, Kubernetes).
• Working knowledge of ML frameworks including TensorFlow, PyTorch, Scikit-learn, TensorRT, and CUDA.
• Experience with messaging queues like Kafka and SQS.
• Proficiency with datastores including Elasticsearch, Mongo, Postgres, DynamoDB, Redis, VectorDB, and GraphDB.
• Bachelor's, Master's, or PhD in Computer Science or a related field.

Preferred Skills

• Experience in large-scale production systems and distributed computing.
• Familiarity with data engineering practices and data warehousing solutions.

Key Skills

Education

Bachelor's degree in Computer Science