Professional Summary
Senior Machine Learning Engineer with 8 years of experience designing and deploying production-grade models on AWS and GCP. Specialises in deep learning, MLOps pipelines, and real-time inference systems that power customer-facing features. Combines technical expertise with strong communication skills to align data science initiatives with business objectives.
Skills
- Machine Learning
- AWS
- Deep Learning
- Python
- Pytorch
- Tensorflow
- Communication
- Azure
- GCP
- GO
- Agile
- R
- MLOps
- Feature Engineering
Experience
Senior Machine Learning Engineer — Elife Transfer
- Architected real-time recommendation engine serving 10M monthly active users, improving click-through rates by 15%.
- Drove migration of model serving infrastructure from SageMaker to Kubeflow, reducing deployment time from days to hours.
- Implemented end-to-end observability for ML pipelines, decreasing drift detection time from weeks to days.
- Mentored junior data scientists on feature engineering best practices, increasing team velocity by 30%.
- Collaborated with product teams to define success metrics, ensuring experiments adhered to growth objectives.
Machine Learning Engineer — Turiyam AI
- Designed and shipped fraud detection models that reduced false positives by 40% without sacrificing genuine transaction volume.
- Established MLOps best practices, automating retraining and validation cycles for 6 production models.
- Partnered with engineering teams to embed real-time scoring APIs, supporting 5M daily transactions with sub-100ms latencies.
- Conducted regular knowledge-sharing sessions on NLP advancements, elevating team capabilities in language models.
- Optimised feature pipelines using Spark and Airflow, cutting latency from hours to minutes for analytical dashboards.
Professional Summary
Senior Machine Learning Engineer with 8 years of experience designing and deploying production-grade models on AWS and GCP. Specialises in deep learning, MLOps pipelines, and real-time inference systems that power customer-facing features. Combines technical expertise with strong communication skills to align data science initiatives with business objectives.
Skills
- Machine Learning
- AWS
- Deep Learning
- Python
- Pytorch
- Tensorflow
- Communication
- Azure
- GCP
- GO
- Agile
- R
- MLOps
- Feature Engineering
Experience
Senior Machine Learning Engineer — Elife Transfer
- Architected real-time recommendation engine serving 10M monthly active users, improving click-through rates by 15%.
- Drove migration of model serving infrastructure from SageMaker to Kubeflow, reducing deployment time from days to hours.
- Implemented end-to-end observability for ML pipelines, decreasing drift detection time from weeks to days.
- Mentored junior data scientists on feature engineering best practices, increasing team velocity by 30%.
- Collaborated with product teams to define success metrics, ensuring experiments adhered to growth objectives.
Machine Learning Engineer — Turiyam AI
- Designed and shipped fraud detection models that reduced false positives by 40% without sacrificing genuine transaction volume.
- Established MLOps best practices, automating retraining and validation cycles for 6 production models.
- Partnered with engineering teams to embed real-time scoring APIs, supporting 5M daily transactions with sub-100ms latencies.
- Conducted regular knowledge-sharing sessions on NLP advancements, elevating team capabilities in language models.
- Optimised feature pipelines using Spark and Airflow, cutting latency from hours to minutes for analytical dashboards.
Professional Summary
Senior Machine Learning Engineer with 8 years of experience designing and deploying production-grade models on AWS and GCP. Specialises in deep learning, MLOps pipelines, and real-time inference systems that power customer-facing features. Combines technical expertise with strong communication skills to align data science initiatives with business objectives.
Skills
- Machine Learning
- AWS
- Deep Learning
- Python
- Pytorch
- Tensorflow
- Communication
- Azure
- GCP
- GO
- Agile
- R
- MLOps
- Feature Engineering
Experience
Senior Machine Learning Engineer — Elife Transfer
- Architected real-time recommendation engine serving 10M monthly active users, improving click-through rates by 15%.
- Drove migration of model serving infrastructure from SageMaker to Kubeflow, reducing deployment time from days to hours.
- Implemented end-to-end observability for ML pipelines, decreasing drift detection time from weeks to days.
- Mentored junior data scientists on feature engineering best practices, increasing team velocity by 30%.
- Collaborated with product teams to define success metrics, ensuring experiments adhered to growth objectives.
Machine Learning Engineer — Turiyam AI
- Designed and shipped fraud detection models that reduced false positives by 40% without sacrificing genuine transaction volume.
- Established MLOps best practices, automating retraining and validation cycles for 6 production models.
- Partnered with engineering teams to embed real-time scoring APIs, supporting 5M daily transactions with sub-100ms latencies.
- Conducted regular knowledge-sharing sessions on NLP advancements, elevating team capabilities in language models.
- Optimised feature pipelines using Spark and Airflow, cutting latency from hours to minutes for analytical dashboards.
Professional Summary
Senior Machine Learning Engineer with 8 years of experience designing and deploying production-grade models on AWS and GCP. Specialises in deep learning, MLOps pipelines, and real-time inference systems that power customer-facing features. Combines technical expertise with strong communication skills to align data science initiatives with business objectives.
Skills
- Machine Learning
- AWS
- Deep Learning
- Python
- Pytorch
- Tensorflow
- Communication
- Azure
- GCP
- GO
- Agile
- R
- MLOps
- Feature Engineering
Experience
Senior Machine Learning Engineer — Elife Transfer
- Architected real-time recommendation engine serving 10M monthly active users, improving click-through rates by 15%.
- Drove migration of model serving infrastructure from SageMaker to Kubeflow, reducing deployment time from days to hours.
- Implemented end-to-end observability for ML pipelines, decreasing drift detection time from weeks to days.
- Mentored junior data scientists on feature engineering best practices, increasing team velocity by 30%.
- Collaborated with product teams to define success metrics, ensuring experiments adhered to growth objectives.
Machine Learning Engineer — Turiyam AI
- Designed and shipped fraud detection models that reduced false positives by 40% without sacrificing genuine transaction volume.
- Established MLOps best practices, automating retraining and validation cycles for 6 production models.
- Partnered with engineering teams to embed real-time scoring APIs, supporting 5M daily transactions with sub-100ms latencies.
- Conducted regular knowledge-sharing sessions on NLP advancements, elevating team capabilities in language models.
- Optimised feature pipelines using Spark and Airflow, cutting latency from hours to minutes for analytical dashboards.
Professional Summary
Senior Machine Learning Engineer with 8 years of experience designing and deploying production-grade models on AWS and GCP. Specialises in deep learning, MLOps pipelines, and real-time inference systems that power customer-facing features. Combines technical expertise with strong communication skills to align data science initiatives with business objectives.
Skills
- Machine Learning
- AWS
- Deep Learning
- Python
- Pytorch
- Tensorflow
- Communication
- Azure
- GCP
- GO
- Agile
- R
- MLOps
- Feature Engineering
Experience
Senior Machine Learning Engineer — Elife Transfer
- Architected real-time recommendation engine serving 10M monthly active users, improving click-through rates by 15%.
- Drove migration of model serving infrastructure from SageMaker to Kubeflow, reducing deployment time from days to hours.
- Implemented end-to-end observability for ML pipelines, decreasing drift detection time from weeks to days.
- Mentored junior data scientists on feature engineering best practices, increasing team velocity by 30%.
- Collaborated with product teams to define success metrics, ensuring experiments adhered to growth objectives.
Machine Learning Engineer — Turiyam AI
- Designed and shipped fraud detection models that reduced false positives by 40% without sacrificing genuine transaction volume.
- Established MLOps best practices, automating retraining and validation cycles for 6 production models.
- Partnered with engineering teams to embed real-time scoring APIs, supporting 5M daily transactions with sub-100ms latencies.
- Conducted regular knowledge-sharing sessions on NLP advancements, elevating team capabilities in language models.
- Optimised feature pipelines using Spark and Airflow, cutting latency from hours to minutes for analytical dashboards.
Professional Summary
Senior Machine Learning Engineer with 8 years of experience designing and deploying production-grade models on AWS and GCP. Specialises in deep learning, MLOps pipelines, and real-time inference systems that power customer-facing features. Combines technical expertise with strong communication skills to align data science initiatives with business objectives.
Skills
- Machine Learning
- AWS
- Deep Learning
- Python
- Pytorch
- Tensorflow
- Communication
- Azure
- GCP
- GO
- Agile
- R
- MLOps
- Feature Engineering
Experience
Senior Machine Learning Engineer — Elife Transfer
- Architected real-time recommendation engine serving 10M monthly active users, improving click-through rates by 15%.
- Drove migration of model serving infrastructure from SageMaker to Kubeflow, reducing deployment time from days to hours.
- Implemented end-to-end observability for ML pipelines, decreasing drift detection time from weeks to days.
- Mentored junior data scientists on feature engineering best practices, increasing team velocity by 30%.
- Collaborated with product teams to define success metrics, ensuring experiments adhered to growth objectives.
Machine Learning Engineer — Turiyam AI
- Designed and shipped fraud detection models that reduced false positives by 40% without sacrificing genuine transaction volume.
- Established MLOps best practices, automating retraining and validation cycles for 6 production models.
- Partnered with engineering teams to embed real-time scoring APIs, supporting 5M daily transactions with sub-100ms latencies.
- Conducted regular knowledge-sharing sessions on NLP advancements, elevating team capabilities in language models.
- Optimised feature pipelines using Spark and Airflow, cutting latency from hours to minutes for analytical dashboards.
Professional Summary
Senior Machine Learning Engineer with 8 years of experience designing and deploying production-grade models on AWS and GCP. Specialises in deep learning, MLOps pipelines, and real-time inference systems that power customer-facing features. Combines technical expertise with strong communication skills to align data science initiatives with business objectives.
Skills
- Machine Learning
- AWS
- Deep Learning
- Python
- Pytorch
- Tensorflow
- Communication
- Azure
- GCP
- GO
- Agile
- R
- MLOps
- Feature Engineering
Experience
Senior Machine Learning Engineer — Elife Transfer
- Architected real-time recommendation engine serving 10M monthly active users, improving click-through rates by 15%.
- Drove migration of model serving infrastructure from SageMaker to Kubeflow, reducing deployment time from days to hours.
- Implemented end-to-end observability for ML pipelines, decreasing drift detection time from weeks to days.
- Mentored junior data scientists on feature engineering best practices, increasing team velocity by 30%.
- Collaborated with product teams to define success metrics, ensuring experiments adhered to growth objectives.
Machine Learning Engineer — Turiyam AI
- Designed and shipped fraud detection models that reduced false positives by 40% without sacrificing genuine transaction volume.
- Established MLOps best practices, automating retraining and validation cycles for 6 production models.
- Partnered with engineering teams to embed real-time scoring APIs, supporting 5M daily transactions with sub-100ms latencies.
- Conducted regular knowledge-sharing sessions on NLP advancements, elevating team capabilities in language models.
- Optimised feature pipelines using Spark and Airflow, cutting latency from hours to minutes for analytical dashboards.
Professional Summary
Senior Machine Learning Engineer with 8 years of experience designing and deploying production-grade models on AWS and GCP. Specialises in deep learning, MLOps pipelines, and real-time inference systems that power customer-facing features. Combines technical expertise with strong communication skills to align data science initiatives with business objectives.
Skills
- Machine Learning
- AWS
- Deep Learning
- Python
- Pytorch
- Tensorflow
- Communication
- Azure
- GCP
- GO
- Agile
- R
- MLOps
- Feature Engineering
Experience
Senior Machine Learning Engineer — Elife Transfer
- Architected real-time recommendation engine serving 10M monthly active users, improving click-through rates by 15%.
- Drove migration of model serving infrastructure from SageMaker to Kubeflow, reducing deployment time from days to hours.
- Implemented end-to-end observability for ML pipelines, decreasing drift detection time from weeks to days.
- Mentored junior data scientists on feature engineering best practices, increasing team velocity by 30%.
- Collaborated with product teams to define success metrics, ensuring experiments adhered to growth objectives.
Machine Learning Engineer — Turiyam AI
- Designed and shipped fraud detection models that reduced false positives by 40% without sacrificing genuine transaction volume.
- Established MLOps best practices, automating retraining and validation cycles for 6 production models.
- Partnered with engineering teams to embed real-time scoring APIs, supporting 5M daily transactions with sub-100ms latencies.
- Conducted regular knowledge-sharing sessions on NLP advancements, elevating team capabilities in language models.
- Optimised feature pipelines using Spark and Airflow, cutting latency from hours to minutes for analytical dashboards.
Professional Summary
Senior Machine Learning Engineer with 8 years of experience designing and deploying production-grade models on AWS and GCP. Specialises in deep learning, MLOps pipelines, and real-time inference systems that power customer-facing features. Combines technical expertise with strong communication skills to align data science initiatives with business objectives.
Skills
- Machine Learning
- AWS
- Deep Learning
- Python
- Pytorch
- Tensorflow
- Communication
- Azure
- GCP
- GO
- Agile
- R
- MLOps
- Feature Engineering
Experience
Senior Machine Learning Engineer — Elife Transfer
- Architected real-time recommendation engine serving 10M monthly active users, improving click-through rates by 15%.
- Drove migration of model serving infrastructure from SageMaker to Kubeflow, reducing deployment time from days to hours.
- Implemented end-to-end observability for ML pipelines, decreasing drift detection time from weeks to days.
- Mentored junior data scientists on feature engineering best practices, increasing team velocity by 30%.
- Collaborated with product teams to define success metrics, ensuring experiments adhered to growth objectives.
Machine Learning Engineer — Turiyam AI
- Designed and shipped fraud detection models that reduced false positives by 40% without sacrificing genuine transaction volume.
- Established MLOps best practices, automating retraining and validation cycles for 6 production models.
- Partnered with engineering teams to embed real-time scoring APIs, supporting 5M daily transactions with sub-100ms latencies.
- Conducted regular knowledge-sharing sessions on NLP advancements, elevating team capabilities in language models.
- Optimised feature pipelines using Spark and Airflow, cutting latency from hours to minutes for analytical dashboards.
Professional Summary
Senior Machine Learning Engineer with 8 years of experience designing and deploying production-grade models on AWS and GCP. Specialises in deep learning, MLOps pipelines, and real-time inference systems that power customer-facing features. Combines technical expertise with strong communication skills to align data science initiatives with business objectives.
Skills
- Machine Learning
- AWS
- Deep Learning
- Python
- Pytorch
- Tensorflow
- Communication
- Azure
- GCP
- GO
- Agile
- R
- MLOps
- Feature Engineering
Experience
Senior Machine Learning Engineer — Elife Transfer
- Architected real-time recommendation engine serving 10M monthly active users, improving click-through rates by 15%.
- Drove migration of model serving infrastructure from SageMaker to Kubeflow, reducing deployment time from days to hours.
- Implemented end-to-end observability for ML pipelines, decreasing drift detection time from weeks to days.
- Mentored junior data scientists on feature engineering best practices, increasing team velocity by 30%.
- Collaborated with product teams to define success metrics, ensuring experiments adhered to growth objectives.
Machine Learning Engineer — Turiyam AI
- Designed and shipped fraud detection models that reduced false positives by 40% without sacrificing genuine transaction volume.
- Established MLOps best practices, automating retraining and validation cycles for 6 production models.
- Partnered with engineering teams to embed real-time scoring APIs, supporting 5M daily transactions with sub-100ms latencies.
- Conducted regular knowledge-sharing sessions on NLP advancements, elevating team capabilities in language models.
- Optimised feature pipelines using Spark and Airflow, cutting latency from hours to minutes for analytical dashboards.
Professional Summary
Senior Machine Learning Engineer with 8 years of experience designing and deploying production-grade models on AWS and GCP. Specialises in deep learning, MLOps pipelines, and real-time inference systems that power customer-facing features. Combines technical expertise with strong communication skills to align data science initiatives with business objectives.
Skills
- Machine Learning
- AWS
- Deep Learning
- Python
- Pytorch
- Tensorflow
- Communication
- Azure
- GCP
- GO
- Agile
- R
- MLOps
- Feature Engineering
Experience
Senior Machine Learning Engineer — Elife Transfer
- Architected real-time recommendation engine serving 10M monthly active users, improving click-through rates by 15%.
- Drove migration of model serving infrastructure from SageMaker to Kubeflow, reducing deployment time from days to hours.
- Implemented end-to-end observability for ML pipelines, decreasing drift detection time from weeks to days.
- Mentored junior data scientists on feature engineering best practices, increasing team velocity by 30%.
- Collaborated with product teams to define success metrics, ensuring experiments adhered to growth objectives.
Machine Learning Engineer — Turiyam AI
- Designed and shipped fraud detection models that reduced false positives by 40% without sacrificing genuine transaction volume.
- Established MLOps best practices, automating retraining and validation cycles for 6 production models.
- Partnered with engineering teams to embed real-time scoring APIs, supporting 5M daily transactions with sub-100ms latencies.
- Conducted regular knowledge-sharing sessions on NLP advancements, elevating team capabilities in language models.
- Optimised feature pipelines using Spark and Airflow, cutting latency from hours to minutes for analytical dashboards.