JABREAL JOHNSON

Forward Deployed Engineer · Applied AI / ML Platform — Remote (US)

· jabrealmoe@gmail.com · https://jabrealjohnson.com · GitHub

PROFILE

Software/ML engineer who ships AI into production and works inside the customer's problem. Long track

record across applied AI/ML, cloud & platform engineering, DevOps/CI-CD and product delivery — RAG/LLM

systems, production ML pipelines, and sub-second APIs — plus a habit of embedding with teams and

coaching them to adopt the solution (1,100+ engineers trained). Owns the whole arc: messy problem →

design → deploy → stay until it works.

Applied AI / LLM: RAG (LangChain, Pinecone/Weaviate/Chroma), fine-tuning (LoRA/QLoRA), GPT / LLaMA / Mistral / Falcon, Hugging Face Transformers, prompt engineering, evaluation harnesses (human feedback, BLEU/ROUGE), vector databases, recommendation systems, NLP (NER, sentiment analysis, summarization).
ML / MLOps: TensorFlow, PyTorch, Scikit-learn, DeepSpeed, Accelerate, vLLM, quantization (4/8-bit), MLflow, Ray, model versioning, A/B + canary deploys, drift/bias monitoring, feature engineering.
Cloud & Platform: AWS (SageMaker, Bedrock, EKS, ECS, Lambda, Glue, EMR, Redshift, Kinesis, DynamoDB, S3, VPC, CDK, CloudFormation), GCP (Vertex AI), Azure ML; Docker, Kubernetes, OpenShift; Terraform.
DevOps / CI-CD: Jenkins / CloudBees, GitHub Actions, GitLab CI, Bamboo, Azure DevOps; Artifactory; Puppet; SonarQube; TDD/BDD/ATDD (Gherkin, Cucumber, JUnit, Mockito, Playwright).
Data: ETL architecture, Spark, Kafka (AWS MSK), Apache Flink, PostgreSQL, Redis, ElasticSearch, real-time streaming, data quality.
Languages: Python, Java, Go, TypeScript/JavaScript, C++, Scala, SQL.
Product / Delivery: product ownership & discovery, roadmaps / OKRs, agile coaching (Scrum, Kanban, SAFe), stakeholder & executive communication, enablement at scale, Atlassian suite mastery (Jira, Jira Align, Confluence).

Built RAG pipelines (LangChain + Pinecone) lifting answer accuracy ~30%.
Deployed production recommendation engines at 10M+ daily predictions; +34% engagement; $18M incremental revenue.
Shipped sub-200ms ML-backed API microservices for credit decisioning and fraud detection.
Cut model inference latency ~40% via vLLM + 4/8-bit quantization.
Drove 90% enterprise MLOps adoption; HIPAA-compliant deployment; reduced diagnostic assessment time ~45%.
Delivered risk-classification models (~89% accuracy) across a $2B+ premium portfolio; saved $22M+/yr.
Trained and coached 1,100–1,500+ engineers; enabled 50–90 teams at scale; TDD raised deployment quality ~70%.
Cut build/deploy time 40–50% and manual deployments ~65%.

AWS Certified Solutions Architect — Associate & Professional · AWS Certified Cloud Practitioner
AWS Certified Machine Learning — Specialty
Atlassian Certified Jira Administrator (ACP-100)
ICP-ACC Certified Agile Coach
M.S. Decision Science · B.S. Computer Science · B.A. English (Rhetoric & Composition)
Languages: English, French, Arabic