John Adegbuji
@johnadegbuji
Senior AI/ML software engineer building production-scale LLM and multimodal systems end to end.
United StatesWhat I'm looking for
I’m a Senior AI/ML Software Engineer with 9+ years building and shipping large-scale AI systems across Meta, Amazon, Microsoft, and JPMorgan Chase. I specialize in the full LLM and generative-AI stack—fine-tuning (SFT, RLHF/DPO, LoRA), retrieval-augmented generation, and agentic orchestration—plus multimodal image/video generation and the inference optimizations needed to run at real scale.
In recent roles, I helped grow an AI assistant to 1B+ monthly users across Meta’s apps, and I led production agentic workstreams by setting technical direction, mentoring engineers, and delivering a tool-calling/function-routing layer that cut end-to-end latency 30%. I’ve also modernized Facebook Groups Search with hybrid lexical-semantic retrieval (nDCG@10 lift 9%), built evaluation-gated release harnesses, and implemented safety/quality monitoring to catch silent regressions before they ship.
Experience
Work history, roles, and key accomplishments
Built and optimized Meta generative-AI assistant capabilities at billion-user scale, including fine-tuning (SFT/RLHF-DPO/LoRA), hybrid RAG, agentic orchestration, multimodal understanding, and inference cost/latency optimizations. Served as tech lead for production AI workstreams and implemented monitoring with online A/B tests and safety/quality scoring.
Developed generative-AI assistant and ad-creative generation products for Amazon sellers, including Bedrock-based RAG grounding, tool-use agent behavior, backend APIs, and evaluation/guardrails for safe rollout. Built full-stack creative workflows with diffusion-based image/video generation and quality/safety filters.
Worked on AI product features including GitHub Copilot, Azure OpenAI Service, and Nuance DAX Copilot. Delivered low-latency IDE and model-serving experiences with streaming inference, RAG grounding, responsible-AI guardrails, and domain-specific clinical documentation generation with safety controls.
Designed and implemented a tokenization-as-a-service security platform for regulated data, including API versioning, microservice architecture, data modeling for low-latency token lookups, encryption key lifecycle management, and RBAC controls. Built partner-facing onboarding and audit tooling and supported production incident response for security-critical operations.
Built the PCRF Automation GUI to automate telecom onboarding workflows, including REST APIs, database models, admin dashboards, and asynchronous provisioning jobs. Improved reliability and reduced manual processing time for internal teams through validation, logging, and audit-ready workflows.
Education
Degrees, certifications, and relevant coursework
Seattle University
Bachelor of Science, Electrical Engineering
2013 - 2017
Earned a Bachelor of Science in Electrical Engineering from Seattle University from 2013 to 2017.
Tech stack
Software and tools used professionally
OpenAPI
Apache Spark
GitHub
Kubernetes
MySQL
PostgreSQL
MongoDB
Rollout
Node.js
Django
.NET Core
Ruby on Rails
Next.js
.NET
Redis
Terraform
jQuery
JavaScript
Java
HAML
PyTorch
Django REST framework
FastAPI
GraphQL
gRPC
pytest
GuardRails
Storefronts
s3-lambda
SQL
Hugging Face
Temporal
Pydantic
Harness
Score
GitHub Copilot
Scale AI
pgvector
Agentic
Faiss
LangGraph
Make
Keep
PEFT
Lexical
Task
Safe
Availability
Location
United StatesAuthorized to work in
Job categories
Skills
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