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Machine Learning (ML) / MLOps Engineer Job Description

An ML/MLOps Engineer is responsible for designing, developing, and deploying machine learning models and pipelines. Here's a brief overview:

Key Responsibilities

- Model Development: Design and develop machine learning models using various algorithms and techniques.

- MLOps: Implement and manage machine learning pipelines, including data preparation, model training, and deployment.

- Model Deployment: Deploy machine learning models to production environments, ensuring scalability and reliability.

- Model Monitoring: Monitor model performance and retrain models as needed to maintain accuracy and relevance.

- Collaboration: Collaborate with cross-functional teams, including data scientists, product managers, and engineers.

Technical Skills

- Machine Learning: Strong understanding of machine learning concepts, including supervised and unsupervised learning, deep learning, and reinforcement learning.

- Programming: Proficiency in programming languages like Python, R, or Julia.

- ML Frameworks: Experience with machine learning frameworks like TensorFlow, PyTorch, or scikit-learn.

- MLOps Tools: Familiarity with MLOps tools like TensorFlow Extended (TFX), MLflow, or Kubeflow.

- Cloud Platforms: Experience with cloud platforms like AWS, Azure, or GCP.

• Design, develop, and maintain data pipelines and ETL workflows on AWS platform
• Work with AWS services like S3, Glue, Lambda, Redshift, EMR, and Athena for data ingestion, transformation, and analytics
• Collaborate with Data Scientists, Analysts, and Business teams to understand data requirements
• Optimize data workflows for performance, scalability, and reliability
• Troubleshoot data issues, monitor jobs, and ensure data quality and integrity
• Write efficient SQL queries and automate data processing tasks
• Implement data security and compliance best practices
• Maintain technical documentation and data pipeline monitoring dashboards

Causality Biomodels is an Indo-German life science informatics company that focuses on the development of data-based solutions in the bioinformatics sector. Specifically, we work using semantic integration & information extraction methods, knowledge & data organization, and advanced statistical & machine learning techniques in the context of life sciences.

The team Causality Biomodels is searching for a full stack developer with a strong focus on Python, capable of taking on a lead developer role.

You will be mainly focusing on the following areas:

• Implementing new features by modifying our backend system and UI according to the product backlog and discussions with the team.
• Rapid prototyping to explore new directions based on current research developments.
• Design, development and maintenance of APIs, as well as product and add-on components.
• Maintenance of code integrity and organization.

The requirements are:

• Successfully completed bachelor’s or master’s degree in computer science or in related fields such as Bioinformatics.
• At least 2 years of professional software engineering experience.
• High proficiency in Python and ability to write clean and well-documented code (must).
• Experience with cloud-based development using AWS (preferred), GCP or Azure.
• Experience with Docker and container-based deployment.
• Proficiency in JavaScript.
• Experience with at least one database system (SQL or no-SQL).
• High familiarity with Git.
• Experience with agile development practices.
• Experience with CI/CD and automated testing.
• Very strong English skills (both verbal and written).

Bonus points for:

• Knowledge about machine learning or data science.
• Experience with Python packages SpaCy, scikit-learn, flask and fastapi.
• Experience with JavaScript libraries React and Redux/Context.
• Experience with Gitlab CI/CD pipelines.
• Experience working with knowledge graph data.
• Knowledge and experience in bioinformatics methods.