THE ROLE:
As a Senior Full-stack Machine Learning Developer (Energy Practice), you will have the opportunity to work with leading institutions in the utilities, energy, and industrial sectors, deploying cutting-edge, end-to-end ML & AI solutions. You will tackle complex challenges in predictive maintenance, asset optimization, energy demand forecasting, and industrial automation, leveraging AI to drive operational efficiency, cost savings, and sustainability in Oil & Gas, Renewable Energy, and large-scale infrastructure management.
THE PERSON:
We are highly selective about who we bring on board and appreciate your attention to the following:
- Submitting a tailored CV
- Completing a 60-second video interview explaining what sets you apart
- Participating in a technical assessment
- (Optional) Sharing a GitHub project you are particularly proud of
At a high level, the ideal candidate possesses the following qualifications:
- 7+ years of experience in a consulting or BigTech environment, deploying ML, Deep Learning, NLP, Classification, Regression, and Generative AI use cases across the utilities, energy, and industrial sectors.
- Full-stack expertise in Python, Java, JavaScript, or Julia for ML model development, with backend infrastructure experience in data staging, transformation, and deployment across AWS, Azure, and Snowflake.
- Extensive experience working with large-scale industrial datasets, including SCADA systems, IoT sensor data, geospatial datasets, time-series data, and energy consumption logs.
- Strong AI & ML domain expertise in predictive maintenance, energy demand forecasting, asset health monitoring, power grid optimization, and supply chain automation.
KEY RESPONSIBILITIES:
- Develop and deploy ML, Deep Learning, NLP, Classification, Regression, and Generative AI solutions for utilities, Oil & Gas, and renewable energy applications, including predictive maintenance, asset failure detection, grid stability forecasting, and industrial process optimization.
- Build and maintain full-stack AI/ML applications, leveraging Python, Java, JavaScript, or Julia, with backend infrastructure expertise in data staging, transformation, and deployment across AWS, Azure, and Snowflake.
- Work with large-scale industrial datasets, including sensor data, geospatial information, power grid telemetry, and time-series predictive models, ensuring high-quality data processing and model training.
- Lead and contribute to the full AI/ML development lifecycle, including supervised, semi-supervised, unsupervised, and reinforcement learning use cases.
- Optimize and deploy deep learning models, particularly transformers, using frameworks like PyTorch and TensorFlow.
- Architect and manage scalable backend infrastructure, ensuring robust data pipelines and cloud/on-premises enterprise architecture.
- Communicate technical insights effectively in a client-facing role, requiring native English or C2 proficiency and strong presentation skills, including experience engaging with senior management.
- Thrive in fast-paced, evolving environments, managing tight deadlines and ambiguous requirements.
- Periodic travel may be necessary.
PREFFERED EXPERIENCE:
- Experience in Computer Vision, particularly in industrial inspections, pipeline monitoring, and defect detection using AI.
- Expertise in training and fine-tuning large language models, including distillation and supervised fine-tuning techniques, for industrial NLP applications such as equipment maintenance logs, operational risk analysis, and compliance automation.
- Proficiency in federated learning and privacy-preserving ML, with a focus on industrial IoT security, decentralized energy forecasting, and predictive maintenance optimization.
- Hands-on experience in robotics or machine vision for automated inspections, safety monitoring, and predictive maintenance in energy and manufacturing environments.
ACADEMIC CREDENTIALS:
Master’s degree (ideally PhD) in Computer Science, Machine Learning, Data Science, or a related field.
Love what we do but not finding a position that connects with you? Drop us an email with a CV and a few words about yourself. If we like what we see, we will get in touch. hi@serious.co
