Chief Technology Officer

Benicann is looking for a hands-on CTO candidate with a proven track record in the fields of

computer vision and AI. We are looking for leadership skills to guide the technical direction of

the company, manage a small team, and make strategic decisions. The ideal candidate has

solid execution capabilities and can ensure an end-to-end delivery of a small team that consists

of in-house, freelancers and subcontractors. As CTO at Benicann, you'll need to balance your

hands-on work with overseeing the technical aspects of the entire organization. Since you’ll be

working in a small company, you will be required to wear multiple hats, so your role as CTO

might involve tasks beyond the technical realm. Be prepared to be flexible and adaptable in your

approach.

Responsibilities:

● Vision and Strategy: As a CTO, you'll be responsible for shaping the company's

technical vision and long-term strategy. You'll need to identify market trends, assess

potential growth areas, and align the technical roadmap with the company's goals.

● Technical Expertise: Since you want to be hands-on, it's crucial to have a strong

technical background in both computer vision and AI. This includes knowledge of

machine learning algorithms, neural networks, deep learning frameworks, and various

computer vision techniques.

● Hiring and Team Building: As the company grows, you might need to build a technical

team. Hiring individuals who complement your skills and share your passion for the

company's vision is important.

● Product Development: You'll be responsible for translating technical expertise into

tangible products or solutions. This could involve working closely with the product and

design teams to create innovative offerings that address customer needs.

● Risk Management: Understand and manage the technical risks associated with the

projects you undertake. This could involve assessing the feasibility of a project,

estimating timelines, and identifying potential roadblocks.

Requirements:

● TensorFlow and Deep Learning Frameworks: You should be well-versed in popular deep

learning frameworks like TensorFlow, PyTorch, and Keras. Understanding how to design,

train, and fine-tune neural networks is crucial for building AI models.

● NVIDIA Jetson: Since you're focused on computer vision, familiarity with NVIDIA Jetson

platforms (such as Jetson Xavier NX) is important. These platforms are optimized for AI

at the edge and can be used to deploy computer vision models on devices.

● MLOps (Machine Learning Operations): Understanding MLOps practices is vital for

deploying, monitoring, and maintaining machine learning models in production. This

includes version control, model deployment pipelines, continuous integration/continuous

deployment (CI/CD), and monitoring.

● AWS (Amazon Web Services) for Big Data: AWS offers a range of services for big data

processing and storage. Familiarity with services like Amazon S3, EMR (Elastic

MapReduce), Redshift, and AWS Glue is essential for handling large-scale data in your

AI projects.

● Computer Vision Algorithms and Techniques: Having a deep understanding of computer

vision algorithms such as image segmentation, object detection, and image classification

is crucial. Stay up-to-date with the latest techniques, such as convolutional neural

networks (CNNs) and transformer-based models for vision tasks.

● GPU Acceleration: Proficiency in utilizing GPUs for accelerated deep learning training

and inference is important. This includes leveraging CUDA and cuDNN libraries for GPU

computing.

● Data Preprocessing and Augmentation: A solid grasp of data preprocessing and

augmentation techniques is necessary for preparing diverse and high-quality training

datasets for your AI models.

● Distributed Computing: Understanding how to scale your computations across multiple

machines or nodes is important for tackling complex AI tasks efficiently.

● Containerization and Orchestration: Skills in containerization technologies like Docker

and container orchestration tools like Kubernetes are valuable for deploying and

managing AI applications in various environments.

● Data Security and Privacy: Given the sensitivity of data in AI projects, having a strong

understanding of data security and privacy principles is important. This includes

knowledge of encryption, access controls, and compliance regulations.

● Real-time Systems: If your company's applications involve real-time decision-making,

understanding how to design and implement low-latency systems is crucial.

● Performance Optimization: Being able to optimize your AI models and applications for

better performance, both in terms of speed and accuracy, is an important skill.

Interpersonal Skills:

● Collaboration: In a small company, collaboration is key. You'll likely work closely with the

CEO, other executives, and the technical team to ensure that everyone is aligned and

working towards the same objectives.

● Communication Skills: Clearly communicating complex technical concepts to both

technical and non-technical stakeholders is crucial. This skill helps in building trust and

maintaining transparency within the company.

● Networking: Connect with professionals in the computer vision and AI fields. This can

help you stay informed about industry developments, potential partnerships, and even

potential clients.

● Continuous Learning: Technology evolves rapidly. Make sure to allocate time for

continuous learning to stay ahead of the curve and adapt to new tools and techniques.