Open to opportunities

Kevin Hassan

@kevinhassan

Message

Senior AI/ML engineer building production MLOps and NLP/RAG systems that deliver measurable business impact.

United States

Message

What I'm looking for

I’m looking for a team where I can own end-to-end AI systems—design to deployment—using production-grade MLOps, strong governance, and scalable cloud infrastructure, partnering cross-functionally to align models with clear KPIs.

I’m a Senior AI/ML Engineer with 7+ years of experience owning the design, deployment, and scaling of enterprise-grade AI systems. I’ve led cross-functional initiatives and mentoring while aligning technical roadmaps with strategic KPIs, specializing in deep learning, NLP, RAG architectures, and predictive analytics.

Most recently, I architected and led an enterprise geospatial intelligence platform predicting truck wait times within ±20 minutes, reducing dwell time by 15% across 120K+ facilities. I’ve built production-grade MLOps and governance (MLflow registry, CI/CD with Jenkins, Docker, Terraform, drift detection, and blue/green Kubernetes deployments) and delivered real-time inference using FastAPI, Kafka streams, and Prometheus/Grafana observability—while also shipping RAG-based assistants and recommendation systems that materially improved automation, latency, and revenue.

Experience

Work history, roles, and key accomplishments

Current

Senior AI/ML Engineer

Current

Cipherlogics

Dec 2023 - Present (2 years 6 months)

Architected an enterprise geospatial intelligence platform predicting truck wait times within ±20 minutes, reducing dwell time by 15% across 120K+ facilities. Led end-to-end ETA forecasting with PyTorch/Spark on AWS, improved generalization by 18% via drift-aware retraining, and delivered zero-downtime Kubernetes releases with production MLOps and SLO-driven monitoring.

PyTorch Apache Spark AWS fastAPI Kafka MLFlow Kubernetes Blue Green Deployment

ML Engineer

Snorkel AI

Jan 2022 - Nov 2023 (1 year 10 months)

Guided architecture and deployment of an AI virtual assistant that resolved 70% of inquiries autonomously, cutting response time by 70% and support costs by 30%. Built hybrid RAG pipelines with LangChain, vector databases, and embeddings, deployed inference on AWS ECS with Terraform, and used MLflow plus CI/CD to govern model promotion and rollback.

Lang Chain Langchain Agents Vector Databases Embedding Pipelines Docker AWS ECS Terraform MLFlow A B Testing

Junior ML Engineer

Bazaarvoice

Mar 2019 - Dec 2021 (2 years 9 months)

Developed a hybrid recommendation engine (collaborative filtering + content embeddings) increasing CTR by 60% and revenue per visitor by 10%. Migrated a legacy rule-based system to API-driven ML services with FastAPI, Docker, and Kubernetes, reducing production release cycles to under one month.