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I’m Ebube Imoh, an AI/ML Engineer specializing in agentic architectures, multi-agent systems, and autonomous decision-making pipelines. Over 4+ years delivering production-grade AI systems across fintech, media, and edtech, I automate operational workflows, reduce support loads, and improve fraud detection. I’m proficient in Python, LangChain, TensorFlow, PyTorch, Hugging Face, and deploying scalable RAG, NLP, and CV pipelines. I excel at turning research into high-impact engineering solutions. In cross-functional settings, I translate ideas into practical, scalable AI solutions and enjoy collaborating to solve real-world problems with autonomous, adaptable systems.…I’m Ebube Imoh, an AI/ML Engineer specializing in agentic architectures, multi-agent systems, and autonomous decision-making pipelines. Over 4+ years delivering production-grade AI systems across fintech, media, and edtech, I automate operational workflows, reduce support loads, and improve fraud detection. I’m proficient in Python, LangChain, TensorFlow, PyTorch, Hugging Face, and deploying scalable RAG, NLP, and CV pipelines. I excel at turning research into high-impact engineering solutions. In cross-functional settings, I translate ideas into practical, scalable AI solutions and enjoy collaborating to solve real-world problems with autonomous, adaptable systems.

Ebube Imoh

AI Engineer, Developer, Data Scientist, +3

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I’m Ebube Imoh, an AI/ML Engineer specializing in agentic architectures, multi-agent systems, and autonomous decision-making pipelines. Over 4+ years delivering production-grade AI systems across fintech, media, and edtech, I automate operational workflows, reduce support loads, and improve fraud detection. I’m proficient in Python, LangChain, TensorFlow, PyTorch, Hugging Face, and deploying scalable RAG, NLP, and CV pipelines. I excel at turning research into high-impact engineering solutions. In cross-functional settings, I translate ideas into practical, scalable AI solutions and enjoy collaborating to solve real-world problems with autonomous, adaptable systems.…I’m Ebube Imoh, an AI/ML Engineer specializing in agentic architectures, multi-agent systems, and autonomous decision-making pipelines. Over 4+ years delivering production-grade AI systems across fintech, media, and edtech, I automate operational workflows, reduce support loads, and improve fraud detection. I’m proficient in Python, LangChain, TensorFlow, PyTorch, Hugging Face, and deploying scalable RAG, NLP, and CV pipelines. I excel at turning research into high-impact engineering solutions. In cross-functional settings, I translate ideas into practical, scalable AI solutions and enjoy collaborating to solve real-world problems with autonomous, adaptable systems.

Available to hire

I’m Ebube Imoh, an AI/ML Engineer specializing in agentic architectures, multi-agent systems, and autonomous decision-making pipelines. Over 4+ years delivering production-grade AI systems across fintech, media, and edtech, I automate operational workflows, reduce support loads, and improve fraud detection. I’m proficient in Python, LangChain, TensorFlow, PyTorch, Hugging Face, and deploying scalable RAG, NLP, and CV pipelines. I excel at turning research into high-impact engineering solutions.

In cross-functional settings, I translate ideas into practical, scalable AI solutions and enjoy collaborating to solve real-world problems with autonomous, adaptable systems.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Language

Work Experience

Machine Learning Engineer at Flutterwave

April 1, 2025 - November 7, 2025

Designed and implemented an agentic automation framework using LangChain and OpenAI APIs, embedding Multi-Context Planning (MCP) for intelligent task routing and Agent-to-Agent (A2A) communication for seamless data handoff. The solution automated over 60% of daily operational tasks in payment resolution, increasing throughput by 45. Built and deployed a RAG-powered customer support chatbot integrated with Pinecone and FAISS, handling 5 K+ weekly user queries and reducing ticket volume by 40% while cutting resolution time by 35. Engineered a fraud detection model combining ML insights and rule-based heuristics, improving catch rate by 15% and reducing false positives by 10. Optimized model deployment pipelines using AWS Lambda and CI/CD, halving update latency and enabling continuous training with near-zero downtime.

AI Research Scientist at FilmIt Project

January 1, 2025 - January 1, 2025

Architected a multi-stream video analysis pipeline integrating YOLO, ResNet, and emotion recognition models for real-time content tagging; improved metadata accuracy by 15. Designed a real-time scene indexing system using DeepFace and ResNet embeddings, achieving 92% facial identification accuracy across diverse datasets. Developed an automated MCP-inspired evaluation framework for batch testing video AI models, reducing testing time by 20. Authored research briefs on emotion-aware AI and presented findings to cross-disciplinary teams.

Machine Learning Engineer at Dataset Nexus Tech

July 1, 2024 - July 1, 2024

Developed a serverless chatbot using AWS Lambda and Amazon Lex with BERT-based intent classification, reducing support ticket resolution time by 40. Created an AI tutoring assistant with adaptive difficulty via autonomous decision-making, boosting learner engagement scores by 50. Delivered an NLP-to-video content generation system, reducing course production time by 46. Implemented CI/CD pipelines for ML models using Docker, GitHub Actions, and AWS, enabling seamless deployment and monitoring.

Machine Learning Engineer at Flutterwave

April 1, 2025 - November 19, 2025

Designed an agentic automation framework (LangChain, MCP, A2A communication) that automated 60% of daily payment-resolution workflows, increasing operational throughput by 45%. Built and deployed a RAG-powered customer support system (Pinecone, FAISS) handling 5K+ weekly queries, reducing ticket volume by 40% and resolution time by 35%. Engineered a hybrid fraud-detection model combining ML insights with rule-based heuristics, boosting fraud catch-rate by 15% while reducing false positives by 10%. Optimized deployment pipelines (AWS Lambda, CI/CD) to cut update latency by 50% and support near-zero downtime for retraining.