Principal Software Engineer (AI and Time Series Data Specialist)

As a Principal Software Engineer you will be responsible for leading the development and implementation of complex high-quality software solutions with embedded AI with large volumes of Timeseries Data.

You will work closely with cross-functional teams to ensure the delivery of robust and scalable applications. Your expertise will be crucial in guiding the technical direction of projects and mentoring junior engineers.

Key Responsibilities:

• Lead the development of complex software systems in the area of AI and Timeseries Data.
• Collaborate with product managers designers and other stakeholders to define detailed project requirements and deliverables.
• Provide technical leadership and mentorship to software engineering teams on AI and Timeseries Practices.
• Ensure the quality and performance of software through code reviews testing and best practices.
• Stay updated with the latest industry trends and technologies in AI and large volume timeseries data to drive innovation.
• Troubleshoot and resolve technical issues in a timely manner.

Qualifications :

1) Time Series & Signal Foundations

• Strong understanding of statistical time series analysis and forecasting fundamentals including common model families (e.g. classical statistical and modern ML/DL approaches).
• Working knowledge of signal processing fundamentals including filtering frequency-domain thinking sampling/resampling concepts and noise characteristics.

2) Time Series Machine Learning Capabilities

• Fluency across core time series problem types:
  • Forecasting (uni-/multi-variate multi-step)
  • Anomaly detection (point/contextual/collective)
  • Event detection / change-point detection
  • Classification (states modes fault categories)
  • Regression / soft sensors (predicting one signal from others)
• Strong feature engineering ability for time series data (lags rolling features seasonal encodings domain transforms) plus familiarity with when deep models reduce or replace manual features.

3) Data Engineering for Time Series (End-to-End)

• Experience building time series data pipelines including:
  • Ingestion and streaming patterns (event time vs processing time concepts ordering late data)
  • Storage and query design for time series workloads (downsampling retention aggregation strategy)
  • Data quality semantics and metadata management (units calibration/scaling missingness tagging conventions asset hierarchy)

4) Databases & Data Modeling

• Solid understanding of relational databases and modeling (schemas indexing query performance).
• Understanding of graph databases and where they fit (asset relationships topology dependency networks hierarchy traversal).

5) MLOps & Production Deployment

• Experience operationalizing ML for time series including:
  • Deployment patterns (batch vs streaming inference service-based inference)
  • Monitoring (data drift model drift performance/latency alert volumes)
  • Model/version lifecycle practices (reproducibility rollback strategies controlled rollouts)

6) Domain Knowledge (Sensor Industrial/Operational Context)

• Strong practical understanding of sensor data characteristics and real-world behavior:
  • Sensor physics and function noise/failure modes calibration issues
  • Operational context such as control loops setpoints and how process changes appear in data
• Ability to translate domain constraints into model features evaluation methods and alerting logic.

7) Programming & Core Technical Stack

• Deep expertise in Python including analysis and ML frameworks (e.g. NumPy/pandas PyTorch and related tooling).
• Proficiency in multiple programming languages used in production systems (e.g. TypeScript C# Go Python).
• Strong software engineering foundation: design testing maintainability and best practices.

8) Platform Engineering & Cloud-Native Delivery

• Familiarity with containerization and cloud-native deployment approaches (e.g. Docker/Kubernetes patterns).
• Understanding of stream processing frameworks (e.g. Spark Flink Stream Analytics) and how they support near-real-time scoring and analytics.
• Experience with at least one major cloud platform (Azure/AWS/GCP) including deploying and operating data/ML workloads.

9) Experience & Education

• Bachelors or Masters degree in Computer Science Engineering or related field (or equivalent practical experience).
• Extensive software development experience ideally with significant focus on time series platforms and/or applied AI systems.

10) Professional & Leadership Skills

• Excellent problem-solving skills and attention to detail especially in diagnosing data issues and model behavior.
• Strong communication skillsable to explain model outcomes trade-offs and risks to both technical and non-technical stakeholders.
• Demonstrated leadership abilities including technical ownership mentoring and cross-team collaboration.

Additional Information :

We embrace flexibility and hybrid work opportunities to support diverse needs and lifestyles while also valuing inclusive workplace experiences. By fostering a sense of community we drive innovation strengthen connections and nurture belonging. Our commitment ensures you can work in a way that suits you best while also engaging with colleagues to share ideas and build meaningful relationships.

Remote Work :

No

Employment Type :

Full-time