***ATTN: MUST HAVE MsC or PhD from top-tier university in an engineering faculty
**Remote / Hybrid possible
Have you ever wanted to lead AI initiatives at the frontier of engineering, working on the critical infrastructure that underpins modern civilization? Are you driven by solving high-stakes challenges in renewable energy, power utilities, and oil & gas — across the Middle East, Africa, Europe, and the U.S. — with the freedom of remote work, strong compensation, and equity upside?
We are a boutique consultancy built by PhD engineers, Fortune 500 AI veterans, and applied researchers from ABB, Meta, Harvard, and MIT. While Silicon Valley has optimized ad clicks, we’re focused on engineering AI for energy reliability, grid modernization, decarbonization, and industrial resilience.
We’re looking for serious builders — those who can translate industrial complexity into engineered AI solutions and deploy them in environments where reliability isn’t optional.
We are seeking other builders, who can decompose complex challenges, drive AI solutions from ideation to execution, and challenge conventional wisdom.
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 PROCESS:
We are highly selective about who we bring on board and appreciate your attention to the following:
Submitting a tailored CV
If we like you, we will invite to a 60-second video interview explaining what sets you apart / technical assessment
If we like you, we will invite you for 2 interviews: one with the Head of Energy, the other with CEO.
At a high level, the ideal candidate possesses the following qualifications:
5+ 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 from a top-tier university (ideally PhD) in Computer Science, Machine Learning, Data Science, or a related field.
