AI Engineer - Remote

Overview

Join Cynomi as our first full-time AI engineer and help transform our platform with cutting-edge AI technologies. You’ll play a key role in developing and integrating innovative AI solutions to enhance our products and services. Collaborate with cross-functional teams to explore and implement the latest AI advancements, driving meaningful impact on the company’s growth and success.

In Short

• Design and implement AI solutions tailored to business needs, including Generative AI and Large Language Models (LLMs).
• Develop and optimise Retrieval-Augmented Generation (RAG) architectures and fine-tune model outputs.
• Build and deploy AI agents and infrastructure to support training and deployment of AI models.
• Optimise workflows for large datasets, distributed training, and model evaluations.
• Collaborate with teams to integrate AI-driven features into products.
• Stay current with AI trends, security best practices, and share knowledge with the team.
• Document architectures, processes, and lessons learned effectively.

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 or Java.
• Strong understanding of model evaluation techniques, including Confusion Matrices, and metrics like accuracy, precision, recall, and F1-score.
• Familiarity with data preprocessing, feature engineering, and model tuning.
• 2+ years experience applying AI to practical and comprehensive technology solutions.
• 5+ years relevant experience such as software development.

Benefits

• 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.