Open to opportunities

Jason Ford

@jasonford1

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Senior Machine Learning Engineer building production-grade GenAI and ML systems.

United States

Message

What I'm looking for

I want to build and ship reliable, high-performance ML/GenAI systems at scale—especially RAG and LLM infrastructure—working with strong MLOps/DevOps practices, observability, and reproducible deployments in a remote, distributed environment.

I’m a Senior Machine Learning Engineer with 12+ years building production-grade AI systems, scalable data pipelines, and cloud-native infrastructure. I focus on end-to-end ML delivery—from data ingestion and feature engineering through model training, deployment, and production monitoring.

My work centers on Large Language Models and RAG architectures, with hands-on strength in Generative AI tooling and performance optimization. I’ve designed infrastructure and workflows that make teams faster to iterate, including reproducible LLM serving environments, quantized model deployments, and unified serving stacks.

I bring a strong MLOps and DevOps foundation, shipping reliable, high-performance systems at scale using Kubernetes, Terraform, Docker, and CI/CD automation on AWS. I’ve led remote, distributed engineering efforts and emphasize observability, security (RBAC), and reproducibility to keep deployments stable across environments.

Experience

Work history, roles, and key accomplishments

Senior Machine Learning Engineer

Perficient

Jun 2021 - Jun 2026 (5 years)

Designed reproducible LLM serving environments using Ollama and built optimized llama.cpp deployments with int4/int8 quantized models. Implemented AWS infrastructure as code and Kubernetes GitOps workflows with ArgoCD, plus production observability and RBAC for secure inference.

Ollama fastAPI Pydantic SQLAlchemy LLamacpp Quantization (4 Bit 8 Bit)Hugging Face Transformers SpiceDB BentoML Terraform Docker GitHub Actions Helm Kubernetes ArgoCD GitOps RBAC Prometheus Grafana AWS ECR ECS S3 RDS EFS

Machine Learning Engineer

Hexagon

Sep 2016 - May 2021 (4 years 8 months)

Built ML solutions across telehealth and savings/investment domains, including patient matching, NLP triage, time-series forecasting, and customer segmentation. Delivered production deployments with Docker/Kubernetes/EKS, automated CI/CD with Jenkins, and implemented retraining triggers using CloudWatch drift alerts.

scikit learn Random Forest Logistic Regression A B Testing NLTK SpaCy ARIMA Docker Kubernetes Amazon EKS Jenkins Cloudwatch PySpark Kinesis S3 XGBoost K Means AWS Lambda SQS TensorFlow Autoencoders EC2

Python Developer

Brookfield Asset Management

Jul 2014 - Aug 2016 (2 years 1 month)

Built backend modules in Python using Django and Flask, improving request handling performance and implementing RESTful APIs. Created automated ETL workflows with Pandas/NumPy and deployed services on AWS EC2 with S3/RDS using Fabric for repeatable environment setup.

Python Django Flask REST APIs MySQL PostgreSQL SQLAlchemy Pandas NumPy EC2 S3 AWS RDS Fabric PIP

Automated Document Processing & RAG

The Hartford

Architected a plugin-based document intelligence platform supporting RAG, summarization, and custom NLP processing modes on AWS. Built an event-driven ingestion flow into OpenSearch and connected retrieval to Bedrock via prompt engineering.

Summarization S3 AWS Step Functions AWS Lambda OpenSearch K NN Vector Search Prompt Engineering Terraform

Genomics and Clinical ML

LabCorp

Developed genomic and clinical ML pipelines using SageMaker managed training with Spark-based preprocessing for predictive biomarker discovery. Containerized inference on EKS with FastAPI/gRPC and Istio mTLS, automating CI/CD and hyperparameter tuning while tracking experiments in MLflow.

TensorFlow PyTorch Amazon SageMaker PySpark AWS Glue Amazon EKS fastAPI gRPC Istio MLFlow GitHub Actions CI CD ECR Prometheus Grafana

LLM Infrastructure Platform

Perficient

Built an Ollama-based LLM infrastructure to provide isolated, reproducible local and production-ready serving environments for teams. Enabled end-to-end deployment workflows with infrastructure as code and Kubernetes automation.

Ollama fastAPI Pydantic SQLAlchemy Microservices LLamacpp INT8 INT4 Docker Kubernetes Terraform GitHub Actions Helm ArgoCD GitOps RBAC

Savings and Investment ML Platform

Acorns

Built AWS EMR ETL pipelines to ingest high-volume transactions and trained XGBoost models to predict monthly saving propensity. Implemented clustering-based segmentation and Isolation Forest fraud detection with serverless routing, and automated nightly retraining with Dockerized GPU jobs.

PySpark Kinesis S3 XGBoost K Means Isolation Forest AWS Lambda SQS TensorFlow Autoencoders EC2 Docker

Telehealth ML Platform

Amwell

Developed patient-provider matching models using scikit-learn and improved assignment outcomes via iterative A/B testing. Built NLP pipelines for intake message classification and auto-triage, and used ARIMA forecasting to support ECS autoscaling policies.