Machine Learning Infrastructure Engineer

Holisticon Connect is a division within NEXER GROUP, a custom software development company started in Poland in 2017 with a team of over 140 people. The company works with world-renowned brands from Scandinavia, the UK, and Western Europe. The role is part of a team building a highly advanced drug discovery platform at the intersection of machine learning, large-scale data systems, and computational science, focusing on AI-driven drug design and processing massive biological and chemical datasets.

• 5+ years of professional experience in Machine Learning Engineering, Data Engineering, or ML Infrastructure roles
• Strong proficiency in Python and its scientific ecosystem (NumPy, Pandas, PyTorch)
• Proven experience building and operating production-grade ML systems in cloud environments
• Solid understanding of MLOps practices, including model registries, feature stores, CI/CD for ML, and deployment patterns
• Hands-on experience with workflow orchestration tools such as Argo Workflows or Prefect
• Strong experience with cloud data warehouses (BigQuery or Snowflake), including performance tuning and complex SQL optimization
• Experience working in cloud-native environments (preferably GCP) and Kubernetes-based infrastructure
• Familiarity with AI-assisted development workflows / coding agents used in production environments
• Ability to communicate effectively with both engineering teams and scientific/research stakeholders

Nice to have:

• Background in cheminformatics (e.g. RDKit, molecular featurization) or computational biology
• Experience with distributed computing frameworks such as Ray / Anyscale
• Knowledge of agentic AI systems and LLM-based automation workflows
• Experience in scientific, research-driven or high-performance ML environments

• Build and scale infrastructure for deploying ML models (e.g. GNNs, Transformers) in chemistry and structural biology domains
• Productionize research-grade models into robust, scalable, and maintainable systems
• Design and maintain cloud-native data architectures (BigQuery / Snowflake) for large-scale molecular datasets
• Develop and orchestrate ML pipelines using tools such as Argo Workflows or Prefect
• Implement and optimize distributed training and inference workflows using Ray / Anyscale
• Contribute to development and deployment of predictive models within a computational drug discovery platform
• Design and maintain LLM-based agentic systems supporting automated drug design workflows
• Collaborate closely with ML infrastructure and research teams to ensure alignment between scientific needs and engineering standards