Senior Machine Learning Engineer

We are looking for a Senior Machine Learning Engineer with strong hands-on experience in MLOps, production ML systems, PySpark, and Databricks.

The role is focused more on deploying, operationalizing, monitoring, and maintaining ML/DL models in production than on pure research or model development.

Key Responsibilities

• Design, deploy, and operationalize Machine Learning and Deep Learning models in production.
• Build and maintain end-to-end MLOps workflows, covering model training, validation, deployment, monitoring, versioning, and retraining.
• Develop scalable data and ML pipelines using PySpark and Databricks.
• Productionize Deep Learning models and support scalable model inference.
• Work with multimodal data, combining Computer Vision, textual data, metadata, and other signals to build ML solutions.
• Implement model monitoring, performance tracking, drift detection, experiment tracking, and lifecycle management.
• Work with cloud-based ML infrastructure and deployment platforms.
• Collaborate with Data Scientists, ML Engineers, Data Engineers, and business stakeholders to deliver production-ready ML solutions.
• Ensure ML systems are scalable, reliable, maintainable, and suitable for production environments.

Mandatory Skills

• 5+ years of experience in Machine Learning / ML Engineering.
• Strong hands-on MLOps and production ML deployment experience.
• PySpark – mandatory.
• Databricks – mandatory.
• Experience deploying Deep Learning models into production.
• Strong understanding of the end-to-end MLOps lifecycle.
• Experience working with Computer Vision + textual data + metadata or other multimodal data.
• Understanding and practical experience with embeddings, vector databases, and semantic search.
• Experience with at least one cloud platform such as AWS, Azure, or GCP.

Good to Have

• Strong understanding of Deep Learning architectures.
• Experience building Deep Learning models using PyTorch or TensorFlow.
• Experience with Generative AI / RAG.
• Experience with MLflow or similar ML lifecycle/experiment tracking tools.
• Experience with Kubernetes, CI/CD, model serving, or ML monitoring platforms.