Description

Lead R&D and product roadmap execution by converting model requirements into production-ready ML solutions. Build scalable infrastructure, manage MLOps pipelines, and mentor engineering teams.

Responsibilities

• Develop APIs and microservices to integrate ML models into production systems.
• Build feature pipelines and manage end-to-end MLOps for training, validation, and monitoring.
• Implement model compression, quantization, and distributed training techniques.
• Design scalable ML infrastructure in collaboration with cloud architects and DevOps.
• Mentor machine learning engineers and conduct code reviews to maintain quality.

Required Skills

• 9+ years of experience as a Machine Learning Engineer or Architect.
• Proficiency in Python for model development and data manipulation.
• Experience with Java or Scala for building production systems.
• Hands-on experience with Kafka, SQS, or similar messaging queues.
• Proficiency with MLOps tools such as MLflow, Kubeflow, or Airflow.
• Experience with cloud platforms (AWS, Google Cloud, or Azure).
• Containerization using Docker and Kubernetes.
• Experience with machine learning frameworks like TensorFlow or PyTorch.
• Bachelor's degree in Computer Science or related field.

Preferred Skills

• Proven portfolio of deployed ML models for batch, streaming, and real-time use cases.
• Master's or PhD in Computer Science or a related field.

Key Skills

Education

Bachelor's degree in Computer Science