Principal AI Engineer

A typical day might include the following:

• Be responsible for the evaluation, adoption, and integration of emerging AI technologies and methodologies across the organization

• Architect and be responsible for the development of large-scale, distributed AI systems capable of processing multi-modal biological data

• Architect, Design, develop, and deploy AI models, algorithms, and systems to analyze sophisticated biological and pharmaceutical data.

• Collaborate with multi-functional teams to identify AI opportunities and chip in to project goals, requirements, and work you're doing.

• Create and maintain machine learning models, developing advanced algorithms for tasks such as image analysis, natural language processing, and predictive modeling.

• Design and optimize data pipelines and workflows for efficient data collection, preprocessing, and storage for AI training and evaluation.

• Adhere to standard methodologies in AI model development, deployment, and maintenance, with a focus on performance, scalability, reliability, and regulatory compliance.

• Stay abreast of the latest AI advancements, assessing their potential application and promoting innovation within the organization.

• Engage with external partners to integrate modern AI technologies and tools.

• Share a point of view in AI-related discussions, presenting findings and insights to partners.

• Implement and refine pioneering machine learning models and deep learning architectures.

• Use statistical techniques and data visualization to analyze and interpret AI model outputs, delivering insights to partners.

• Optimize AI models for performance and reliability, considering computational and regulatory constraints.

• Chip in to the development of AI-related procedures, standards, and guidelines within the organization.

• Participate in scientific and technical discussions, presenting findings to various audiences.

• Develop and maintain comprehensive documentation for AI models and systems.

• Communicate sophisticated AI concepts to non-technical partners.

• Mentor and guide senior AI/ML engineers, data scientists, and multi-functional teams in sophisticated AI methodologies

• Lead technical reviews, architectural decisions, and code quality standards across AI projects

• Translate sophisticated AI concepts into business value propositions for non-technical audiences

• Establish partnerships with technology vendors, cloud providers, and AI research organizations

This job might be for you if you:

• Bachelor's, Master's, or Ph.D. degree in Computer Science, Artificial Intelligence, Data Science, or a related field.

• At least 7 years of experience in AI/ML engineering, preferably within the biotechnology, pharmaceutical, or healthcare industry.

• Proven track record in biotechnology, pharmaceutical, or healthcare AI applications with measurable business impact

• Hands on experience in Building Generative AI solutions from scratch using frameworks such as LangChain, LangGraph, CrewAI

• Profound knowledge of large language models, including prompt engineering, fine-tuning, and retrieval-augmented generation (RAG)

• Extensive experience with cloud-native AI platforms (AWS SageMaker, Google Vertex AI, Azure ML) and containerization technologies

• Profound knowledge of AI governance frameworks, ethical guidelines, and regulatory compliance standards for biotechnology applications

• Profound knowledge of machine learning algorithms, deep learning frameworks (e.g., TensorFlow, PyTorch), and statistical methods.

• Proficient programming skills in languages such as Python, Java, R and familiarity with software engineering practices.

• Practical experience in and deploying AI models.

• Familiarity with Agentic AI patterns: ReAct, tool use, planning strategies, memory management, etc.

• Excellent communication developing skills and the ability to collaborate optimally with multi-functional teams.

• Strong problem-solving skills and a results-driven approach.

• Understanding of regulatory compliance related to AI in the biotech and pharma industries is advantageous.

• Knowledge of natural language processing t