Senior / Principal AI Engineer

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Role Summary

We are looking for a Senior / Principal AI Engineer to design, build, and scale applied AI systems for text and voice analysis, with a strong emphasis on time-series and sequential data.

You will own models end-to-end — from data understanding and experimentation to production deployment — working on real-world time-dependent signals such as voice streams, sessions, and evolving sequences.

This is not a pure MLOps role. Infrastructure exists to support high-quality modeling, fast iteration, and reliable deployment — not to replace applied AI ownership.

What You’ll Work On

Applied AI & Modeling (Core Focus)

• 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.

ML Pipelines & Production Systems

• 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.

Time-Series & Sequential Data

• 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.

Data & Platform Collaboration

• Collaborate with data engineering teams on ETL, data quality, and data versioning.
• Contribute to architecture decisions around feature stores and model registries.

Technical Leadership

• 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 This Role Is (and Is Not)

This role is:
✔ applied AI and modeling-first
✔ time-series focused
✔ production-oriented with ownership

This role is not:
✖ pure MLOps
✖ infra-only or DevOps-heavy
✖ research-only with no product impact

Qualifications

Experience

• 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.

Technical Skills

• 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.

Nice-to-Have

• Experience with streaming or near-real-time data.
• Exposure to Spark, Kafka, or similar data frameworks.
• Experience working with voice or audio data.

Soft Skills

• Strong analytical and problem-solving abilities.
• Comfortable owning systems end-to-end.
• Clear communicator who collaborates well across teams.

Education

• Degree in Computer Science, Machine Learning, Data Engineering, or related field.
• Advanced degrees are a plus, but practical experience is key.

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