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Jeremiah Ayensu

Jeremiah Ayensu

AI Engineer and Application Systems Engineer

AI Engineer and Application Systems Engineer; educator and technical mentor

DRAKA.PC
Senior AI Systems Engineer, Generative AI, RAG, Computer Vision & LLMOps

Biography

Jeremiah Ayensu is an AI Engineer and Application Systems Engineer specialising in taking intelligent systems from research and experimentation through engineering, deployment and production operation.

His expertise spans generative modelling, Computer Vision, Retrieval-Augmented Generation (RAG), Large Language Models and LLMOps, complemented by strong software-engineering capability across backend development, API and systems design, cloud infrastructure, databases and enterprise platforms.

Jeremiah has built AI and software systems across healthcare, financial services, telecommunications, energy and the public sector, working with teams and clients across Ghana, Europe and North America.

His engineering approach places strong emphasis on reliability, security, performance and real-world usefulness. Rather than treating AI models as isolated experiments, his work focuses on the complete engineering environment required to turn intelligent capabilities into dependable software services.

At DRAKA Labs, Jeremiah provides a critical bridge between AI research and production engineering, supporting the transition from experimental models and research prototypes into secure, observable, scalable and maintainable AI-enabled applications.

Within DRAKA.PC, his expertise supports the development of the University's AI engineering capability, including generative AI, institutional knowledge systems, intelligent applications, computer vision and the operational infrastructure required to deploy and manage AI responsibly at scale.

Beyond engineering, Jeremiah contributes to the development of emerging technology talent through teaching, technical bootcamps and research, supporting DRAKA Labs' objective of developing the next generation of engineers and applied AI researchers.

Research interests

  • Generative AI
  • Retrieval-Augmented Generation
  • Large Language Models
  • LLMOps
  • Computer Vision
  • AI Evaluation
  • Production AI Engineering

Applied domains

  • Institutional knowledge assistant
  • Student-service assistant
  • Staff-service assistant
  • Intelligent search
  • Policy and regulation retrieval
  • Document classification
  • Document summarisation
  • Information extraction
  • Workflow assistance
  • AI-enabled analytics
  • Computer-vision services
  • Intelligent anomaly detection

Professional expertise

  • Production AI Engineering
  • Generative AI
  • Retrieval-Augmented Generation
  • Large Language Models
  • LLMOps
  • Computer Vision
  • AI Evaluation
  • Backend Engineering
  • API & Systems Design
  • Cloud Infrastructure
  • AI Data Services
  • AI Security & Privacy

DRAKA Labs responsibilities

  • 01Provide a critical bridge between AI research and production engineering, taking models and prototypes into secure, observable, scalable and maintainable AI-enabled applications
  • 02Engineer generative AI, RAG and LLM-based applications grounded in authorised institutional information with appropriate evaluation and guardrails
  • 03Establish LLMOps practices: model configuration, prompt versioning, retrieval configuration, evaluation, observability, cost and latency management, rollback and auditability
  • 04Engineer computer-vision capabilities for classification, detection, analysis and intelligent monitoring
  • 05Design AI-service APIs and orchestration so models and providers can evolve without redesigning consuming applications
  • 06Address AI security and privacy (prompt injection, unauthorised retrieval, data leakage, knowledge-base permissions) with cybersecurity and data-governance specialists
  • 07Contribute to engineering capacity development through teaching, mentorship, bootcamps and applied AI projects

Programme / project responsibility

DRAKA.PC · Senior AI Systems Engineer, Generative AI, RAG, Computer Vision & LLMOps

Provides specialist engineering capability for the design, integration, deployment and operation of AI-enabled services, sitting between AI research and enterprise application engineering, and helping engineer a governed, reusable AI service layer with LLMOps for the University's enterprise environment.