Principal Software Engineer (Timeseries Data Expert)

IFS is a billion-dollar revenue company with 7000+ employees on all continents. Our leading AI technology is the backbone of our award-winning enterprise software solutions, enabling our customers to be their best when it really matters–at the Moment of Service™. Our commitment to internal AI adoption has allowed us to stay at the forefront of technological advancements, ensuring our colleagues can unlock their creativity and productivity, and our solutions are always cutting-edge.

At IFS, we’re flexible, we’re innovative, and we’re focused not only on how we can engage with our customers but on how we can make a real change and have a worldwide impact. We help solve some of society’s greatest challenges, fostering a better future through our agility, collaboration, and trust.

We celebrate diversity and understand our responsibility to reflect the diverse world we work in. We are committed to promoting an inclusive workforce that fully represents the many different cultures, backgrounds, and viewpoints of our customers, our partners, and our communities. As a truly international company serving people from around the globe, we realize that our success is tantamount to the respect we have for those different points of view.

By joining our team, you will have the opportunity to be part of a global, diverse environment; you will be joining a winning team with a commitment to sustainability; and a company where we get things done so that you can make a positive impact on the world.

We’re looking for innovative and original thinkers to work in an environment where you can #MakeYourMoment so that we can help others make theirs. With the power of our AI-driven solutions, we empower our team to change the status quo and make a real difference.

If you want to change the status quo, we’ll help you make your moment. Join Team Purple. Join IFS.

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.

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

• Bachelor’s or Master’s 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 skills—able 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.

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.