Samuel Jaja
ML/GenAI Software Engineer | Multi-Cloud(AWS First)
AI/ML Systems Engineer and Full-Stack Developer with 5+ years of experience building intelligent systems that combine data engineering, machine learning, and cloud infrastructure. Skilled in LLM fine-tuning, Retrieval-Augmented Generation (RAG), and agentic AI using AWS (Lambda, SageMaker, Bedrock, Terraform). Experienced in distributed data processing with Apache Spark and PySpark on Databricks for scalable ML workflows and data lakehouse architectures, with cross-cloud experience in Azure and GCP. Strong background in Python, .NET, FastAPI, and React/Node for developing robust APIs and AI- driven applications. Passionate about creating production-ready, explainable, and compliant AI systems that improve human-computer interaction.
Featured Projects
RevGeni CRM – AI-Powered Sales Pipeline
Built full-stack enterprise SaaS CRM with Next.js 16, TypeScript, PostgreSQL, and GraphQL API serving AI-driven lead discovery, automated email sequences, and pipeline analytics. Integrated GPT-4 and Exa AI for intelligent company search, flexible multi-LLM architecture, and sub-5s AI response times.
PRIME – Multi-Agent Financial Advisor
Enterprise-grade, fully-serverless multi-agent AI platform for autonomous financial investment analysis. Orchestrates 5 agents via SQS and Lambda, distributed cross-region Bedrock setup, RAG pipeline with SageMaker embeddings, and full SaaS UI. Provisioned with Terraform, Langfuse, and Cloudwatch observability.
StructureGPT – RAG System for Building Regulations
Fine-tuned LLaMA-3.1-8b using LoRA and 8-bit quantization for UK Building Regulations compliance. Deployed to production on Hugging Face L4-GPU at $0.8/active hour with RAGAS evaluation.
AI Digital Twin – Production Serverless Architecture
Built full-stack production AI system using serverless AWS (Lambda, API Gateway, Bedrock, CloudFront). Achieved <200ms global response times with Terraform IaC and automated CI/CD.
MCP-Agentic-TraderNet
Autonomous multi-agent trading system with real-time market data integration, AI research tools, and strategy memory for acting on trading opportunities.
RAG Document QA System
Production-grade document question-answering system using AWS Bedrock and LangChain for intelligent document retrieval and analysis.
About Me
Experience
Full-stack | R&D Software Engineer
CypherCrescent | Jan 2022 - Dec 2024
Developed enterprise-grade backend services with 40% cost savings. Enabled 70% platform growth through efficient architectures. Event-Driven Design: Implemented CQRS pattern with MediatR for command/query separation and event notifications, enabling loosely coupled microservices communication via Pub/Sub messaging patterns. Azure DevOps Integration.
Software Engineering Intern (ML & Software Development)
Integral Computing & Research Centre | June 2019 - Dec 2019
Built computation tools using C# for engineering tasks and learnt the fundamentals of both Machine Learning and Software Development.
Education & Certifications
PhD in View (AI & Carbon Accounting)
MSc. Artificial Intelligence & Data Science (Distinction)
University of Hull, UK
Bachelor of Chemical Engineering (B.Eng)
University of Port Harcourt, Nigeria
Certifications:
- AWS Certified AI Practitioner
- AWS Certified Generative AI Professional (Exp Dec 2025)
- Databricks Fundamentals
- Databricks Generative AI Fundamentals