Cephalgo is seeking a Senior or Principal AI Engineer to design, build, and scale applied AI systems for text and voice analysis, with a strong focus on time-series and sequential data. The role emphasizes end-to-end ownership of models—from data understanding and experimentation to production deployment—on real-world time-dependent signals such as voice streams, sessions, and evolving sequences.
What You'll Do
- Design, train, and evaluate ML models for text and voice analysis.
- Work on time-series and sequential modeling problems.
- Own feature engineering, labeling strategies, and evaluation metrics.
- Iterate on models based on real-world data and performance feedback.
- Build and evolve ML pipelines that support experimentation and continuous improvement.
- Deploy models into production and ensure performance, scalability, and stability.
- Implement model monitoring and retraining workflows.
- Analyze and model time-dependent data such as voice signals, sessions, and event sequences.
- Apply time-aware techniques (windowing, aggregation, decay, sequence modeling).
- Improve model behavior as data distributions evolve over time.
- Collaborate with data engineering teams on ETL, data quality, and data versioning.
- Contribute to architecture decisions around feature stores and model registries.
- Influence modeling approaches and technical direction across the product.
- Mentor engineers and raise engineering and modeling standards.
- Work closely with product teams to translate requirements into effective AI solutions.
What We're Looking For
- 5+ years in AI Engineering, Applied Machine Learning, or similar roles.
- Proven experience building and owning production ML models.
- Experience working with text, speech, or other unstructured data.
- Strong programming skills in Python.
- Experience with PyTorch, TensorFlow, or scikit-learn.
- Solid understanding of time-series or sequential modeling techniques.
- Familiarity with ML pipelines and production deployment.
- Degree in Computer Science, Machine Learning, Data Engineering, or related field.
Nice to Have
- Advanced degrees are a plus, but practical experience is key.
- Experience with streaming or near-real-time data.
- Exposure to Spark, Kafka, or similar data frameworks.
- Experience working with voice or audio data.
Technical Stack
- Python
- PyTorch
- TensorFlow
- scikit-learn
- Spark
- Kafka
Benefits & Compensation
- Backed by over €3 million in funding
- Collaboration with leading European partners in AI ethics, healthcare, and regulatory technology
Work Mode
- Local work in Strasbourg
EEO
