Software Developer (n.2) Position (EIC Transition Project)

Company Description ETIA | Causal AI is a European research and innovation initiative focused on creating next-generation Causal AI tools for data-driven insight and decision-making. The project aims to make advanced causal discovery, modeling, and reasoning usable by a broad range of professionals, not just specialists. ETIA goes beyond traditional predictive analytics by helping users understand why events occur, not just what is likely to happen. Its tools are designed to support researchers, analysts, and organizations in moving from correlations to plausible causal relationships, exploring what-if scenarios, identifying root causes, and making better-informed decisions. The name ETIA is derived from the Greek word αιτία, meaning “cause” or “reason,” reflecting the project’s core mission.

We are looking for an engineer to bridge the gap between ML research and production systems. You will integrate, optimize, and operationalize machine learning components within a larger agentic analytics platform.

You will not be responsible for inventing algorithms, but for making them fast, scalable, and production-ready.

• Integrate ML algorithms into production pipelines and APIs
• Optimize model execution (latency, throughput, memory usage)
• Build and maintain model serving infrastructure
• Collaborate with algorithm developers to productionize algorithms
• Develop testing and validation frameworks for ML components
• Implement caching and provenance mechanisms for models code and dataset trails
• Contribute to CI/CD pipelines for model deployment
• Implement regression testing facility for monitoring the analytics engine performance

• Strong Python experience in ML ecosystems (3+ years)
• Experience deploying models in production environments
• Knowledge of model serving patterns (APIs, batch, streaming)
• Familiarity with performance optimization and profiling
• Understanding of ML lifecycle and MLOps practices
• Experience integrating ML systems with backend APIs

• Experience with ML tooling (MLflow, Kubeflow, DVC)
• Familiarity with java and vector databases / embeddings systems
• Experience with real-time inference systems
• Knowledge of parallel/distributed computing

• ML algorithms run efficiently and reliably in production
• Seamless integration between ML, backend, and agent layers
• Scalable inference pipelines with low latency
• Strong observability of model behavior and performance