Chief Technology Officer

Company Overview

At Cntxt, our mission is to accelerate the development of AI applications and the large language models (LLMs) that power them. We believe that to create the best models, high-quality data is essential. We provide enterprises with the tools to customize powerful generative models, unlocking the full potential of AI safely and effectively. Our Data Engine offers comprehensive features for collecting, curating, and annotating data, alongside robust tools for model evaluation and optimization.

Technical Skills Required:

1. Artificial Intelligence and Machine Learning (AI/ML): A deep understanding of AI and ML concepts, algorithms, and techniques is essential. This includes knowledge of supervised learning, unsupervised learning, reinforcement learning, neural networks, deep learning architectures (such as CNNs, RNNs, GANs), natural language processing (NLP), computer vision, and related frameworks like TensorFlow, PyTorch, or scikit-learn.
2. Software Development: Strong proficiency in software development principles, practices, and methodologies is necessary. This includes expertise in programming languages commonly used in AI development, such as Python, Go lang, as well as familiarity with software engineering best practices, version control systems (e.g., Git), and agile development methodologies.
3. Data Engineering and Data Management: Knowledge of data engineering principles, including data collection, preprocessing, feature engineering, and data pipelines, is crucial for handling large-scale datasets effectively. Familiarity with databases (SQL and NoSQL), distributed computing frameworks (such as Apache Spark), and data warehousing concepts is also beneficial.
4. Cloud Computing: Proficiency in cloud computing platforms like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP) is important for deploying and scaling AI applications in the cloud. Skills in containerization technologies (e.gDocker, Kubernetes) and serverless computing are also valuable for optimising infrastructure resources.
5. Model Deployment and Productionisation: Understanding how to deploy machine learning models into production environments is essential. This includes knowledge of model serving frameworks (e.g., TensorFlow Serving, PyTorch Serve), containerization for model deployment, continuous integration/continuous deployment (CI/CD) pipelines, and monitoring/model performance tracking.
6. Data Privacy and Ethics: Awareness of data privacy regulations (e.g., GDPR, CCPA) and ethical considerations in AI development is important for ensuring compliance and responsible use of AI technologies. This includes knowledge of privacy-preserving techniques, bias mitigation strategies, and ethical AI principles.
7. Cybersecurity: Understanding cybersecurity principles and best practices is essential for safeguarding AI systems against potential threats and vulnerabilities. This includes knowledge of secure coding practices, encryption techniques, network security, and threat detection/mitigation strategies.
8. Domain Knowledge: Depending on the specific industry or application domain of the AI company (e.g., healthcare, finance, autonomous vehicles), domain-specific knowledge and expertise may be required to effectively develop and deploy AI solutions that address industry-specific challenges and requirements.