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 Official notification
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