AI Engineer and App Developer (Fractional CTO or Co-Founder)

EQUITY ONLY COMPENSATION. THIS IS A FRACTIONAL CTO OR CO-FOUNDER ROLE OPPORTUNITY FOR A NEW STARTUP

Company DescriptionAn AI startup company developing technology to help streamine regulatory submission processes to the FDA and EMA, and for bioqequivalence determination studies.

Role DescriptionThis is a remote contract role for an AI Engineer / App Developer (Fractional CTO or Co-Founder). As part of this role, you will be responsible for independently developing a customized AI-driven application and solutions for biotech startups and regulatory affairs consulting firms. Your day-to-day tasks will involve using pattern recognition, software and automation engineering, IoT (Info of Things), AI engineering, neural networks, software development, and natural language processing (NLP) techniques to build innovative and efficient applications. Your expertise will contribute to the growth and success of biotech startups in our portfolio.

QualificationsStrong background in pattern recognition, computer science/ computer engineering, and neural networksMastery in software development and experience with relevant programming languages and artificial intelligence programming / engineeringMastery of natural language processing (NLP) and its applicationsAbility to develop independently AI-driven customized applications and solutions involving automationExcellent problem-solving and analytical skillsStrong communication and collaboration abilitiesExperience in the biotech industry is a plusAdvanced degree in computer science, AI, or a related field is REQUIRED. MINIMUM OF MASTERS DEGREE.Core Programming and AI Engineering Skills RequiredPython - Currently the leading programming language in data science, AI, and machine learning fields. Essential for writing scripts and algorithms commonly used in these areas.R - Useful for statistical software development and data analysis, especially relevant in biotech for processing complex datasets.Java - Helps in building more robust, scalable systems and can be important for integrating AI models with existing enterprise environments.C++ - Known for its execution speed, which is critical when the applications involve complex computations and large data.MATLAB - Particularly advantageous for handling matrix operations and advanced mathematical functions integral to many biotech applications.Machine Learning Frameworks RequiredTensorFlow and Keras - Popular frameworks for deep learning that facilitate the creation of advanced predictive and classification models.PyTorch - Known for its flexibility and efficiency in building complex custom machine learning models.Scikit-Learn - Useful for traditional machine learning algorithms like clustering, linear regression, and others frequently used in data analysis.Data Management and Big Data TechnologiesSQL and NoSQL databases (e.g., MongoDB, Cassandra) - Essential for managing datasets efficiently.Hadoop - Foundations in managing big data environments that are common in biotech for dealing with extensive datasets.Apache Spark - An engine for big data processing, capable of handling complex data pipelines necessary in machine learning projects.Statistics and Mathematical FoundationsStatistical Analysis - Strong foundation in statistics to analyze and make predictions based on data.Mathematics - Especially calculus and linear algebra, which are crucial for understanding and implementing machine learning algorithms.Other Technical SkillsAPIs - Experience in developing and working with APIs for integration of different software components.Git - Version control systems are essential for collaborative development projects.Containerization and Virtualization (e.g., Docker, Kubernetes) - Important for deploying applications in diverse environments reliably.Cloud Services (AWS, Azure, GCP) - Proficient in using cloud services for deploying and scaling applications, crucial for startups looking to minimize infrastructure costs.Domain-Specific KnowledgeBioinformatics - Understanding of computational biology, including experience with tools and databases common in the field, is crucial.Systems Biology - Knowledge of biological systems and processes can be a significant advantage for a role in biotechnology.Chemoinformatics - Useful in drug discovery applications where AI/ML models predict molecular behavior.Soft SkillsProblem-solving - Ability to handle complex challenges and devise effective solutions.Communication - Skills necessary to explain complex technical details to non-technical team members and stakeholders.Team Collaboration - Experience in working collaboratively in team settings, crucial for a startup environment where roles can be fluid.

Key Skills

• Full Stack