About The Position

This is a unique opportunity to join Cynomi as our first full-time AI engineer. You will play a pivotal role in shaping the next phase of our platform as we fully embrace recent AI developments. With full support from the management and executive team, you will have the opportunity to influence the strategic direction of AI adoption, drive innovation, and build cutting-edge AI solutions that have a meaningful impact on our products and services. 

As the first dedicated AI engineer, you will set the foundation for AI initiatives, establish best practices, and collaborate closely with cross-functional teams to ensure AI solutions are seamlessly integrated into our platform. This role offers the chance to lead the development of new AI capabilities, explore emerging AI technologies, and make a significant contribution to the company’s growth and success. 

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

• Design and implement AI and Generative AI architectures tailored to business needs.
• 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.
• Work on functional design, process design (including scenario design, flow mapping), prototyping, testing, training, and defining support procedures, in collaboration with an advanced engineering team and executive leadership
• Articulate and document the solutions architecture and lessons learned for each exploration and accelerated incubation 
• Build and maintain infrastructure to support training and deployment of AI models.
• Optimise workflows for training models, including handling large datasets and distributed training environments.
• Perform detailed evaluations of model performance using Confusion Matrices to identify False Positives (FP) and False Negatives (FN).
• Collaborate with cross-functional teams to integrate AI-driven features into products.
• Stay current with AI trends and suggest improvements to existing systems and workflows 
• Guide and implement AI security best practices throughout the infrastructure and application lifecycle. 
• Stay abreast of AI related security developments. 
• Share knowledge and support the wider teams understanding of relevant AI techniques. 
• Communicate effectively with team members and stakeholders. 

Requirements

• Proven experience in AI development, including both Classical AI techniques (e.g., decision trees, regression, clustering) and Generative AI models (e.g., GANs, transformers).
• Proficiency in programming languages such as Python
• Hands-on experience with AI frameworks and libraries, such as Juniper Notebook,TensorFlow, PyTorch, or scikit-learn.
• Strong understanding of model evaluation techniques, including Confusion Matrices, and metrics like accuracy, precision, recall, and F1-score.
• Expertise in building and managing infrastructure for AI, including GPU/TPU environments and distributed computing.
• Familiarity with data preprocessing, feature engineering, and model tuning.
• Ability to analyse large datasets and derive actionable insights.
• 2+ years experience applying AI to practical and comprehensive technology solutions. 
• 5+ years relevant experience such as software development.
• 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 Computer Science, Artificial Intelligence, Data Science, or a related field.
• Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) for AI/ML deployment.
• Knowledge of modern MLOps practices for managing the AI lifecycle.
• Background in developing explainable AI solutions and addressing ethical considerations.
• Understanding of domain-specific AI applications relevant to the industry. 
• Knowledge of security principles and best practices.