ML, Engineer
Core
Design, build, deploy, and scale machine learning models to power data-driven products and intelligent systems.
Role type
Senior Machine Learning Engineer (Production/MLOps)
Builds
Production-ready ML models, scalable AI solutions, and ML pipelines
Domain
Enterprise AI, Data Science, Software Engineering
Deliverable
production ML models
Required skills
Python, Java, Scala, supervised learning, unsupervised learning, deep learning, TensorFlow, PyTorch, Scikit-learn, Docker, Kubernetes, AWS, Azure, GCP, MLflow, Kubeflow, Airflow, SageMaker, Azure ML, Spark, Kafka, Databricks
Preferred skills
NLP, Computer Vision, Generative AI, enterprise-scale AI platforms, Responsible AI governance
Technologies
TensorFlow, PyTorch, Scikit-learn, Docker, Kubernetes, AWS, Azure, GCP, MLflow, Kubeflow, Airflow, SageMaker, Azure ML, Spark, Kafka, Databricks
Responsibilities
Design, develop, train, optimize, and deploy ML models for business use cases; implement feature engineering and model evaluation; build and maintain ML pipelines; monitor model performance and data drift; optimize models for latency and cost; implement A/B testing and experimentation frameworks; apply Responsible AI principles; collaborate with cross-functional teams; participate in architecture discussions and code reviews.
Seniority
Mid-to-Senior, hands-on IC