Machine learning engineer/AI engineer/AI Agent

Responsibilities:

• Collaborate with product managers, software engineers, and operations teams to define AI-driven features that improve data quality and operator workflows in the ELD (Electronic logging device for truck drivers in US) dashboard.

• Analyze historical “broken” vs. “fixed” event data to design and build supervised learning pipelines that identify, classify, and suggest corrections for invalid or non-compliant driving events (e.g., hours-of-service, shift/cycle violations).

• Develop data-processing workflows for cleaning, validating, and labeling telematics time-series data at scale.

• Architect, train, validate, and deploy machine learning models (e.g., anomaly detection, sequence modeling, classification) that can automatically propose fixes or flag events requiring human review.

• Integrate model outputs into the application’s dashboard, providing clear, actionable recommendations and confidence metrics to operators.

• Monitor and evaluate model performance in production; implement retraining strategies and feedback loops based on operator corrections.

• Write clean, maintainable code and documentation for all ML components, and participate in code reviews and knowledge sharing.

• Stay up to date with the latest advances in machine learning and AI, proposing novel approaches to streamline event-correction workflows and reduce operator effort.

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related quantitative field.

• 3+ years of hands-on experience as an ML Engineer or Data Scientist, ideally working with time-series or event-based data.

• Proficiency in Python and ML frameworks such as scikit-learn, TensorFlow, or PyTorch.

• Strong experience with data manipulation and ETL using pandas, SQL, and/or Spark.

• Demonstrated expertise in anomaly detection, sequence modeling (e.g., RNNs, transformers), or classification tasks.

• Familiarity with containerization (Docker) and orchestration tools (Kubernetes); experience deploying models to cloud platforms (AWS, GCP, or Azure).

• Knowledge of CI/CD practices for ML workflows (e.g., MLflow, Kubeflow, GitOps).

• Excellent analytical problem-solving skills, attention to detail, and ability to translate business needs into technical solutions.

• Strong communication skills and ability to work collaboratively in cross-functional teams.

• Preferred: Experience with telematics or transportation-industry data, ELD/FMCSA regulations, and real-time streaming data (e.g., MQTT, Kafka).

Conditions:

• Competitive salary package, aligned with experience and market standards.

• Flexible work arrangements.

• Generous paid time off and standard public holidays.