About The Position

This is an exceptional opportunity to join Cynomi as an AI Researcher, where you will play a central role in advancing our platform by leveraging the latest developments in AI and Generative AI. With full support from the management and executive team, this role allows you to shape the strategic direction of AI adoption, drive cutting-edge innovation, and make a tangible impact on our products and services.

As part of a forward-thinking and collaborative environment, you will focus on developing state-of-the-art Generative AI solutions alongside Classical AI techniques, establishing best practices, and collaborating closely with cross-functional teams. This role offers the chance to explore emerging AI technologies, refine existing AI processes, and contribute significantly to the growth and success of the company.

About us

Cynomi is a fast-growing, Silicon Valley VC-backed cybersecurity startup. Experiencing massive growth over the past year, our SaaS product is used by hundreds of service providers worldwide.

Operating across three continents, Cynomi is leading the vCISO (virtual Chief Information Security Officer) market category with rapidly growing demand for its AI-powered vCISO platform, which empowers service providers (MSPs and MSSPs) to provide high quality cybersecurity services to their customers.

Key Responsibilities

• Develop AI and Generative AI models and algorithms tailored to address business needs and challenges.
• Design and implement experiments to evaluate the effectiveness of AI models.
• Work with Large Language Models (LLMs) to support application needs.
• Implement and optimise Retrieval-Augmented Generation (RAG) architectures to enhance model performance.
• Apply Prompt Engineering techniques to fine-tune LLM outputs and improve usability
• Design, build, and deploy AI Agents within Retrieval-Augmented Generation (RAG) architectures.
• Research, prototype, and deploy Generative AI models, including transformers, GANs, and advanced language models.
• Apply Classical AI techniques, such as clustering, regression, and decision trees, to solve complex problems.
• Build and maintain robust infrastructure to support the training, testing, and deployment of AI models.
• Collaborate with engineering, product, and business teams to integrate AI-driven features into products and workflows.
• Optimise workflows for model training, including managing large datasets, distributed computing environments, and model fine-tuning.
• Perform detailed evaluations of model performance, including precision, recall, and F1-score, using Confusion Matrices and other metrics.
• Stay current with AI advancements, propose innovative solutions, and improve existing systems and workflows.
• Guide and implement AI security best practices, ensuring ethical and secure development across the AI lifecycle.
• Share knowledge and mentor team members on AI techniques and best practices.

Requirements

• Proven experience in AI research and development, with expertise in both Classical AI (e.g., decision trees, regression, clustering) and Generative AI (e.g., GANs, transformers).
• Experience with programming languages such as Python or Java.
• Hands-on experience with AI frameworks and libraries, such as TensorFlow, PyTorch, scikit-learn, and Jupyter Notebook.
• Strong understanding of data preprocessing, feature engineering, and hyperparameter tuning.
• Knowledge of building and managing AI infrastructure, including GPU/TPU environments and distributed systems.
• Experience in evaluating model performance with metrics like accuracy, precision, recall, and Confusion Matrices.
• Ability to analyse large datasets and derive actionable insights.
• 4+ years of experience applying AI techniques to practical business solutions.
• 5+ years of relevant professional experience, such as software development or data science.
• Familiarity with AI security principles and best practices.

Advantages

• Experience with sophisticated RAG and / or agent based systems.
• Experience with graph-based algorithms
• Familiarity with knowledge graphs and graph databases.
• University level qualification in Mathematics, Computer Science, Artificial Intelligence, Data Science, or a related field.
• Experience deploying AI/ML models on cloud platforms such as AWS, Azure, or Google Cloud.
• Knowledge of modern MLOps practices for managing AI/ML lifecycle processes.
• Background in developing explainable AI solutions and addressing ethical considerations in AI.