Lead Data and ML Engineer working directly with customers to design, develop, and operationalize end-to-end trustworthy Machine Learning solutions. Over 10 years of experience in enterprise-scale, data-centric development, working specifically for the last 6 years on developing end-to-end analytics, training, and inference pipelines for machine learning/deep learning and IoT use-cases, helping customers take their data science and ML solutions to production. A seasoned engineering team lead, combining both a strong ML algorithms knowledge with a proven track record for software development and data management.Expertise: Data Engineering, Data Science, TensorFlow, Keras, PyTorch, scikit-learn, Pandas, Dask, Machine Learning, Deep Learning, Federated Learning, Continual Learning, Trustworthy AI, Explainability and Interpretability, AI Ethics, Adversarial ML, Red Teaming ML Models, Risk Assessment for ML/AI, Big Data, Hadoop Ecosystem, Spark, Spark Streaming, Storm, Flink, Beam, Kafka, NiFi, Airflow, Kubeflow, Kubeflow Pipelines, Prefect, Distributed Systems, edge computing, Industrial IoT, Linux, Python, REST API development (Flask, FastAPI, GraphQL), Docker, Kubernetes, Oracle, Postgres, SQL, NoSQL (HBase, Accumulo, Cassandra, Druid), Tableau, Superset, Open Source Software, Automation, Application Architecture, Data Lifecycle, Java, Human-Computer Interaction, Socio-technical systems
Listed skills include Linux, Big Data, Perl, Oracle, and 27 others.