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Senior Machine Learning/AI Engineer with approximately 10 years of experience designing, building, and deploying scalable AI systems across NLP, computer vision, and recommendation domains. Strong expertise in Python, deep learning frameworks (PyTorch, TensorFlow), distributed data processing, and cloud-native MLOps using Docker, Kubernetes, and AWS. Experienced in end-to-end ML pipelines from data engineering and feature optimization to model deployment and monitoring, delivering reliable production-grade solutions.…Senior Machine Learning/AI Engineer with approximately 10 years of experience designing, building, and deploying scalable AI systems across NLP, computer vision, and recommendation domains. Strong expertise in Python, deep learning frameworks (PyTorch, TensorFlow), distributed data processing, and cloud-native MLOps using Docker, Kubernetes, and AWS. Experienced in end-to-end ML pipelines from data engineering and feature optimization to model deployment and monitoring, delivering reliable production-grade solutions.

A L A N M

Data Scientist, AI Engineer, AI Developer, +4

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Senior Machine Learning/AI Engineer with approximately 10 years of experience designing, building, and deploying scalable AI systems across NLP, computer vision, and recommendation domains. Strong expertise in Python, deep learning frameworks (PyTorch, TensorFlow), distributed data processing, and cloud-native MLOps using Docker, Kubernetes, and AWS. Experienced in end-to-end ML pipelines from data engineering and feature optimization to model deployment and monitoring, delivering reliable production-grade solutions.…Senior Machine Learning/AI Engineer with approximately 10 years of experience designing, building, and deploying scalable AI systems across NLP, computer vision, and recommendation domains. Strong expertise in Python, deep learning frameworks (PyTorch, TensorFlow), distributed data processing, and cloud-native MLOps using Docker, Kubernetes, and AWS. Experienced in end-to-end ML pipelines from data engineering and feature optimization to model deployment and monitoring, delivering reliable production-grade solutions.

Available to hire

Senior Machine Learning/AI Engineer with approximately 10 years of experience designing, building, and deploying scalable AI systems across NLP, computer vision, and recommendation domains.
Strong expertise in Python, deep learning frameworks (PyTorch, TensorFlow), distributed data processing, and cloud-native MLOps using Docker, Kubernetes, and AWS. Experienced in end-to-end ML pipelines from data engineering and feature optimization to model deployment and monitoring, delivering reliable production-grade solutions.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Language

Afar

Advanced

Javanese

Advanced

Aragonese

Intermediate

Bashkir

Advanced

Work Experience

Lead Data Scientist Engineer at 47Billion

February 1, 2022 - Present

Led development and deployment of advanced Generative AI, LLM, and Retrieval-Augmented Generation (RAG) systems for enterprise clients across healthcare, finance, and telecom. Designed scalable workflows using LangChain, LangGraph, and vector databases such as Pinecone and FAISS, improving query efficiency by 40–60% and saving 20+ hours/week. Built persistent conversational agents using Llama3.2 and LangSmith, deployed via Azure Kubernetes Service. Implemented end-to-end MLOps pipelines on GCP and Azure for automated training, deployment, and monitoring. Built an LLM evaluation framework using Ragas and custom metrics to reduce hallucinations and improve enterprise grader reliability.

Lead Data Scientist/Engineer at 47Billion

February 1, 2022 - Present

Led development and deployment of advanced Generative AI, LLM, and Retrieval-Augmented Generation (RAG) systems for enterprise clients across healthcare, finance, and telecom. Designed scalable workflows using LangChain, LangGraph, and vector databases (Pinecone, FAISS). Delivered 40-60% improvement in query efficiency and 20+ hours/week time savings through intelligent automation. Built persistent conversational agents using Llama3.2 and LangSmith; deployed via Azure Kubernetes Service. Implemented end-to-end MLOps pipelines on GCP and Azure for automated training, deployment, and monitoring. Partnered with cross-functional teams in Agile to drive scalable AI delivery. Pioneered LLM evaluation frameworks with RAGs and custom metrics to ensure hallucinatio n-free outputs and enterprise-grade reliability.

Senior Data Scientist

May 1, 2019 - January 31, 2022

Developed production-ready ML pipelines using Python and GCP VertexAI from experimentation to deployment. Led and mentored a team of data scientists to deliver scalable models under tight timelines. Built graph-based recommendation and churn prediction models using neural graph architectures. Deployed a hierarchical frequently-bought-together model on 4M+ transactions, increasing basket size by 8%. Designed end-to-end data workflows integrating BigQuery, time-series modeling, and optimization engines, and optimized model performance and inference pipelines to improve latency and production scalability. Architected a real-time CLV forecasting system using gradient boosting, improving retention campaign ROI by 22%. Championed MLOps adoption using MLflow for experiment tracking and model versioning, reducing retraining cycles by 35%.

Senior Data Scientist at Minute7

May 1, 2019 - January 31, 2022

Developed production-ready ML pipelines on Python and GCP Vertex AI from experimentation to deployment. Led and mentored a team of data scientists, ensuring delivery of scalable models under tight timelines. Built graph-based recommendation and churn prediction models using neural graph architectures. Deployed a hierarchical frequently-bought-together model on 4M+ transactions, increasing basket size by 8%. Designed end-to-end data workflows with BigQuery, time-series modeling, and optimization engines. Optimized model performance and inference pipelines for latency and scalability. Architected a real-time customer lifetime value (CLV) forecasting system using gradient boosting, improving retention campaign ROI by 22%. Championed MLOps adoption by implementing MLflow for experiment tracking and model versioning, reducing retraining cycles by 35%.

Data Scientist

June 1, 2016 - April 30, 2019

Built an NLP-based event detection pipeline on live Twitter streams. Integrated Twitter Live API for streaming ingestion and applied entity extraction for event clustering. Implemented burst detection algorithms achieving 85% accuracy for real-time event identification. Applied Dynamic LDA for topic modeling with 82% classification accuracy. Designed scalable data pipelines supporting multi-window temporal analysis and high-volume ingestion. Improved model robustness through feature engineering, evaluation metric tuning, and validation workflows. Engineered a Spark-based streaming architecture on GCP Dataflow to process 50K+ tweets/minute with sub-second latency. Collaborated with product teams to operationalize insights into a customer-facing dashboard, increasing user engagement by 15%. Led a team of four engineers to build a real-time technical initiative referenced as “Evolve Squads FlexTrades Technical”.

Data Scientist at HobbyDB

June 1, 2016 - April 30, 2019

Led a team of four engineers to build a real-time NLP-based event-detection pipeline on live Twitter streams. Integrated Twitter Live API for streaming ingestion and applied entity extraction for event clustering. Implemented burst detection algorithms achieving 85% accuracy in real-time event identification. Applied Dynamic LDA for topic modeling, achieving 82% classification accuracy. Designed scalable data pipelines supporting multi-window temporal analysis and high-volume ingestion. Improved model robustness through feature engineering, evaluation metric tuning, and validation workflows. Engineered a Spark-based streaming architecture on GCP Dataflow to process 50K+ tweets/min with sub-second latency. Collaborated with product to operationalize insights into a customer-facing dashboard, driving 15% increase in user engagement.