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Mobi User

@mobiuser

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Lead Data & Machine Learning Engineer focused on scalable AI systems and MLOps.

United States

Message

What I'm looking for

I seek roles where I can lead delivery of scalable AI systems, implement MLOps best practices, mentor teams, and drive measurable business impact in collaborative, product-driven environments.

I am a Lead Data and Machine Learning Engineer with over 10 years of experience building and scaling end-to-end data and AI solutions across fintech, SaaS, and enterprise environments.

I specialize in designing robust ETL/ELT pipelines, cloud-native architectures on AWS, GCP, and Azure, and production-grade MLOps practices including CI/CD, model monitoring, and cost optimization.

I have delivered measurable outcomes such as improving data velocity by 45%, enhancing model accuracy by 30%, tripling data throughput, and saving $150K+ annually through standardization and automation.

I am passionate about Retrieval-Augmented Generation, LLM fine-tuning, multimodal AI copilots, and leading cross-functional teams to operationalize responsible, observable, and scalable AI systems.

Experience

Work history, roles, and key accomplishments

Current

Senior Machine Learning Engineer

Current

Cogito Tech

Mar 2021 - Present (4 years 8 months)

Designed and deployed an enterprise RAG platform improving retrieval accuracy 35% and reducing latency 40%; built multimodal AI copilots that automated 40% of support workflows and implemented MLOps pipelines that cut model deployment time 50%.

Pinecone MLFlow Kubeflow Airflow Python PyTorch Spark AWS GCP

Machine Learning Engineer

Klear Stack

Jun 2015 - Feb 2021 (5 years 8 months)

Automated ETL and ingestion pipelines reducing prep time 60% and delivered NLP/CV models that improved accuracy 28%; designed real-time analytics on Kafka cutting reporting latency from hours to minutes.

Python Spark Airflow TensorFlow Kafka Docker Terraform AWS Azure GCP

AI Engineer

Diffco

Jul 2012 - May 2015 (2 years 10 months)

Developed fraud-detection models that reduced financial losses by $2.4M annually and built pipelines processing 50M+ transactions/day while cutting false positives 33%.