Automation Engineer - Scientific Data, AI/ML Pipelines & Integration Dev

About the role

Zifo is a global specialist scientific and process informatics services company supporting life sciences, biotech, and pharmaceutical organizations. We enable digital transformation across R&D, manufacturing, and quality by delivering data-driven, scalable, and compliant software solutions.

Responsibilities

• Collaborate with scientists, assay teams, and lab operations to capture end-to-end assay and experimental workflows, from sample onboarding and execution through data ingestion, validation, and downstream analytics
• Translate scientific and operational requirements into well-defined functional, technical, and data requirements for laboratory platforms, system integrations, and next-generation data pipelines
• Design, develop, and maintain Python-based backend services, APIs, microservices, and data pipelines on AWS using FastAPI and supporting frameworks such as Flask or Django, including integrations with scientific systems such as Benchling, Signals, LIMS, ELN, CDS, and SDMS
• Create and optimize SQL and NoSQL data models and build ETL/ELT and next-generation data pipelines to support structured, semi-structured, and high-volume scientific data, analytics, and AI/ML workloads, including dataset preparation, feature engineering, and model integration into pipelines and applications
• Implement and maintain CI/CD pipelines for automated build, testing and deployment
• Ensure solutions meet performance, data integrity, security, and regulatory compliance requirements (e.g., GxP, 21 CFR Part 11)
• Perform code reviews, debugging, and performance optimization
• Coordinate across cross-functional and geographically distributed teams, managing dependencies and ensuring delivery alignment
• Create ready to deliver technical documentation and track deliverables using JIRA and Confluence

Requirements

• Bachelor's or master's degree in computer science, Engineering, Life Sciences with 3-8 years of hands-on experience in Python development with FastAPI
• Proficiency in SQL, including schema design, complex queries, and performance optimization
• Relational databases such as PostgreSQL, MySQL, Oracle, AWS RDS/Aurora, NoSQL databases such as DynamoDB, MongoDB, or equivalent
• Experience with scientific data and laboratory informatics, including familiarity with Benchling or similar scientific data platforms
• AWS experience, including S3, EC2, Lambda, Step Functions, RDS / Aurora, IAM, monitoring, and logging
• Proficiency with Git-based collaborative development, including branch management, pull requests, code reviews, and integration with CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, AWS CodePipeline)
• Hands-on experience with Test-Driven Development and Python testing frameworks such as pytest, unittest, and mocking libraries
• Working knowledge of AI/ML concepts, including data preparation, feature engineering, model integration, and inference workflows
• Exposure to the data and ML libraries such as pandas, NumPy, and scikit-learn (exposure to TensorFlow or PyTorch is a plus)
• Ability to design data models aligned to scientific and assay workflows & integrating scientific or enterprise systems and working directly with scientists or lab users
• Familiarity with containerization (Docker) and modern deployment best practices
• Strong communication, stakeholder engagement, and cross-team coordination skills

Qualifications

• Bachelor's or master's degree in computer science, Engineering, Life Sciences with 3-8 years of hands-on experience in Python development with FastAPI
• Proficiency in SQL, including schema design, complex queries, and performance optimization
• Relational databases such as PostgreSQL, MySQL, Oracle, AWS RDS/Aurora, NoSQL databases such as DynamoDB, MongoDB, or equivalent
• Experience with scientific data and laboratory informatics, including familiarity with Benchling or similar scientific data platforms
• AWS experience, including S3, EC2, Lambda, Step Functions, RDS / Aurora, IAM, monitoring, and logging
• Proficiency with Git-based collaborative development, including branch management, pull requests, code reviews, and integration with CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, AWS CodePipeline)
• Hands-on experience with Test-Driven Development and Python testing frameworks such as pytest, unittest, and mocking libraries
• Working knowledge of AI/ML concepts, including data preparation, feature engineering, model integration, and inference workflows
• Exposure to the data and ML libraries such as pandas, NumPy, and scikit-learn (exposure to TensorFlow or PyTorch is a plus)
• Ability to design data models aligned to scientific and assay workflows & integrating scientific or enterprise systems and working directly with scientists or lab users
• Familiarity with containerization (Docker) and modern deployment best practices
• Strong communication, stakeholder engagement, and cross-team coordination skills

Skills

• Python development with FastAPI
• SQL and NoSQL database management
• Scientific data and laboratory informatics
• AWS cloud services
• Git-based collaborative development
• Test-Driven Development
• AI/ML concepts and libraries
• Containerization and deployment best practices

Benefits

• Accrued vacation
• Medical, dental, vision, and life insurance
• 401(k) with company matching
• Flexible spending accounts

Pay

TBD

Schedule

TBD

Benefits

• Accrued vacation
• Medical, dental, vision, and life insurance
• 401(k) with company matching
• Flexible spending accounts