Remote Senior Machine Learning Engineer

Summary

Join us at Portcast and be part of a high performing team that is shaping the future of the logistics and shipping industry through cutting-edge predictive analytics!

Requirements

• Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related field
• 5+ years of experience in building, deploying, and scaling machine learning models in production environments
• Experience deploying Generative AI models in production environments, with a strong understanding of Retrieval-Augmented Generation (RAG), AI Agents, and expertise in prompt engineering techniques
• Proven experience with the full product lifecycle, taking models from R&D to deployment in fast-paced environments
• Experience working in a product-based company, preferably within a startup environment with early-stage technical product development
• Strong expertise in Python and SQL, along with experience in cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes)
• Familiarity with real-time data processing, anomaly detection, and time-series forecasting in production
• Experience working with large datasets and big data technologies like Spark and Kafka to build scalable solutions
• First-principles thinking and excellent problem-solving skills, with a proactive approach to addressing challenges
• A self-starter mentality, with the ability to take ownership of projects from end to end and work autonomously to drive results
• Excellent communication skills, with the ability to convey complex technical concepts and a strong customer-obsessed mindset

Responsibilities

• Develop and deploy machine learning models from initial research to production, ensuring scalability and performance in live environments
• Own the end-to-end ML pipeline, including data processing, model development, testing, deployment, and continuous optimization
• Design and implement machine learning algorithms that address key business problems that our product focuses on in visibility, prediction, demand forecasting and freight audit
• Ensure reliable and scalable ML infrastructure, automating deployment and monitoring processes using MLOps best practices
• Perform feature engineering, model tuning, and validation to ensure that models are production-ready and optimized for performance
• Build, test, and deploy real-time prediction models, maintaining version control and performance tracking