Bio- AI Engineer
vor 2 Monaten
Please note: to apply for this job requires expertise with Mass spectrometry data OR NGS data. AI focus from other industries is not relevant.
Job Title: AI Engineer
Location: Berlin, Germany (Hybrid Working Available)
Company Overview: The company leverages advanced AI technologies to explore the therapeutic potential of natural compounds, focusing on areas with substantial unmet medical needs.
Role Overview: The AI Platform Engineer will take charge of managing and enhancing the company’s AI platform, a sophisticated system that integrates computational metabolomics, cheminformatics, and knowledge graphs.
This role is crucial in advancing the platform's capabilities to support the discovery and development of new therapies.
Key Responsibilities:
- Knowledge Graph Management: Utilize and manage knowledge graph technologies to support complex data integration and analysis, facilitating the identification of new therapeutic targets.
- Cloud Infrastructure: Implement and maintain cloud-based solutions to support the scalability and flexibility of the platform, ensuring efficient processing and storage of large datasets.
- Metabolomics Analysis: Lead efforts in the analysis of metabolomic data, particularly focusing on mass spectrometry (mass spec) data, to discover neuroactive compounds.
- Platform Management: Oversee the overall AI platform, ensuring all components are functioning optimally. This includes enhancing platform capabilities, improving scalability, and integrating new technologies as needed.
- Project Leadership: Define project goals and lead the team in setting strategic directions, guiding where the platform should go and how to achieve those objectives.
- MLOps Integration: Apply MLOps principles to efficiently transition machine learning models from development into production environments, enabling the delivery of AI-driven products.
Required Skills and Experience:
PHD/ MSc (Machine Learning, Bioinformatics, Mathematics or Physics) + 2/3 years of experience in a company setting.
Advanced proficiency in Python, with experience using libraries and frameworks such as TensorFlow, PyTorch, etc.
Strong background in developing robust data pipelines. (eg. ETL processes, data modeling, and data architecture).
- Expertise in graph database technologies (e.g., RDF, Neo4j) and query languages (e.g., SPARQL, Cypher); familiarity with relational databases is a plus.
Demonstrated experience in deploying machine learning models, e.g. NLP or deep learning, within live production settings.
- Ability to work independently within a cross-functional team, managing tasks and driving projects with minimal supervision.
- Excellent communication skills in English, both written and verbal.
Preferred Qualifications: Experience with life science/drug discovery data and familiarity with biomedical ontologies.
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