THE ROLE:
As a Senior Full-stack Machine Learning Developer (Supply Chain practice), you will have the opportunity to work with leading organizations in supply chain, logistics, and manufacturing, deploying cutting-edge, end-to-end ML & AI solutions. You will tackle complex challenges in demand forecasting, inventory optimization, logistics planning, supply chain resilience, and operational efficiency, leveraging AI to drive cost savings, risk mitigation, and process automation across global supply networks.
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 supply chain and logistics domain.
- Notable contributions to the development of innovative AI technologies, applied on the Oil / Gas / Utilities domain, resulting in multiple patents and intellectual property (IP) filings.
- 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 supply chain datasets, including ERP data, sensor/IoT data, transportation & logistics records, procurement, and demand forecasting models.
- Strong AI & ML domain expertise in supply chain applications, such as demand forecasting, inventory optimization, route optimization, supplier risk assessment, predictive maintenance, and supply chain resilience modeling.
KEY RESPONSIBILITIES
- Develop and deploy ML, Deep Learning, NLP, Classification, Regression, and Generative AI solutions for supply chain applications, including demand forecasting, inventory optimization, logistics planning, supplier risk assessment, and predictive maintenance.
- 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 supply chain datasets, including ERP data, IoT/sensor data, transportation & logistics records, procurement, and warehouse management systems (WMS), 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 and time-series forecasting models, using frameworks like PyTorch and TensorFlow.
- Architect and manage scalable backend infrastructure, ensuring robust data pipelines and cloud/on-premises enterprise architecture for AI-driven supply chain analytics.
- Active contribution to internal research articles and write ups, spotting new IP / Patent opportunities and contributing to scientific journals.
- Communicate technical insights effectively in a client-facing role, requiring native English or C2 proficiency and strong presentation skills, including experience engaging with supply chain executives, procurement teams, and logistics managers.
- Thrive in fast-paced, evolving environments, managing tight deadlines and ambiguous requirements while ensuring business continuity and supply chain resilience.
- Periodic travel may be necessary.
PREFERRED EXPERIENCE
- Experience in Computer Vision, particularly in quality inspection, defect detection, warehouse automation, and logistics monitoring using AI-driven imaging systems.
- Expertise in training and fine-tuning large language models, including distillation and supervised fine-tuning techniques for supply chain optimization, procurement analytics, and logistics decision support.
- Proficiency in federated learning and privacy-preserving ML, with a focus on secure, decentralized data-sharing across suppliers, manufacturers, and logistics partners.
- Hands-on experience in robotics or machine vision, particularly for autonomous warehouse operations, robotic process automation (RPA), and predictive maintenance in manufacturing and logistics.
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
