We are looking for an ML Engineer to join Intelliarts and contribute to an innovative EV charging management platform that powers large-scale charging networks worldwide.
The platform processes real-time telemetry and charging session logs from thousands of stations, creating a comprehensive data foundation for intelligent monitoring and predictive capabilities. As the ML components are just beginning their journey to production, you’ll play a key role in designing and implementing the first generation of machine learning solutions focused on anomaly detection, predictive maintenance, and behavioral pattern recognition.
Your work will enable charging network operators to proactively identify equipment issues, predict potential failures before they occur, and maintain optimal service quality — transforming raw charging data into actionable intelligence that keeps the infrastructure running smoothly.
Intelliarts Ltd. is a boutique software engineering company established in 1999, in Lviv, Ukraine. We launch and support dedicated development teams that help startups and technology companies worldwide to build awesome products. With a flat structure, we don’t offer traditional vertical career growth. Instead, we created a working environment encouraging professional and personal growth: challenging projects, tight integration with teams abroad, and learning from experienced colleagues.
Responsibilities
- Translate business/optimization needs into ML problems
- Design and own end-to-end data/feature pipelines
- Build, validate, and productionize models for forecasting, classification, and anomaly detection on operational data
- Define evaluation metrics and experimentation loops (offline simulation, A/B tests, post-deployment monitoring)
- Integrate models into microservices and optimization workflows (batch/real-time inference, APIs, containers)
- Establish MLOps foundations: versioning, CI/CD, monitoring, and drift detection.
- Collaborate closely with domain/optimization engineers and stakeholders to ensure models drive measurable impact
Personal Profile Overview
- Comfortable owning the full ML lifecycle (problem framing, data, model, deploy, observe)
- Degree in Data Science, Computer Science, Software Engineering, or related field
- Stability in previous employment history, with a tendency to remain with employers for extended periods
- Experience in managing diverse project activities (not just coding, but also requirements analysis, preparing estimations)
- Clear and effective communication skills, both verbal and written, and the ability to convey ideas, information, and messages accurately and efficiently
- Proficiency in fostering effective collaboration and teamwork activities
- Ability to analyze information, assess situations, and make decisions based on sound reasoning and logical evaluation
- Focus on delivering exceptional customer experiences and prioritizing customer satisfaction
- Analytical thinking, problem-solving abilities, and a strategic approach to technical challenges
- Transparency in sharing information within a team and company
- Willingness to acquire new knowledge and insights to enhance professional growth and performance
Required Skills
- 3+ years of hands-on experience in applied machine learning, with a strong focus on classical ML algorithms and structured data
- Experience with Time Series (forecasting, demand prediction) models like ARIMA, Facebook Prophet, LSTM, Gradient Boosting for Time series.
- Deep understanding of unsupervised learning (clustering, anomaly detection). Experience with unsupervised anomaly detection (Isolation Forest, Autoencoders, Gaussian Mixture Models).
- Experience handling high-volume datasets using Big Data technologies like Apache Spark
- Proven track record in MLOps: building end-to-end ML pipelines, deploying models to production, and managing model retraining lifecycles
- Familiarity with cloud-based ML infrastructure (AWS/GCP/Azure)
- Strong expertise in feature engineering for telemetry data (handling irregular timestamps, resampling, missing values)
- Proficiency in error analysis and debugging ML models, including use of metrics like MAE, RMSE, F1, ROC-AUC, and calibration curves
- English Upper-Intermediate (B2)
As A Plus
- Experience with Semi-supervised learning and Self-supervised learning for time-series
- Experience with the energy/EV charging domain
- Survival analysis (Weibull models, Cox models)
- Remaining Useful Life (RUL) estimation
- Experience with Kafka / Kinesis / PubSub and stream processing (Flink, Spark Streaming or similar)
We Offer
- Fuel your professional growth with paid online courses, conferences, certifications, English classes, a corporate library, and a leadership program
- Thrive in a culture of trust and cooperation with no time trackers and minimal bureaucracy
- Enjoy 20 business days of paid vacation, plus state holidays to prioritize your well-being
- Experience an open-door culture, transparent communication, and top management at a handshake distance
- Enjoy comfortable office vibes with no open space policy, relaxing sports areas, a spacious bar/kitchen, and more
- Achieve balance with our hybrid/fully remote work model
- Receive fair and competitive compensation
- Fuel your productivity and foster a sense of community with complimentary daily lunches
- Participate in meaningful initiatives supporting Ukraine’s victory
- Take flexible sick leave without burdensome documentation and access parental benefits
- Choose from comprehensive medical insurance or a sports compensation package
- Have fun with regular team-building activities, corporate events and celebrations, and unique initiatives like Week in Lviv