AI Model Deployment Expert

Machine Learning Engineer

We are seeking a highly skilled Machine Learning Engineer to join our team. As a key member of our engineering team, you will be responsible for designing and deploying machine learning models that drive business value.

Responsibilities:

• Design and deploy machine learning models from 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.
• Develop robust algorithms that address key business problems, particularly 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.

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).
• Experience 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.

Preferred Skills:

• Prediction
• Time series
• Python
• Containerization
• Anomaly Detection
• Data Science
• Deployment
• OCR

About Us

Our company is a venture-backed startup that predicts global trade flows to help logistics and shipping companies become more profitable. We offer a fast-paced, innovative environment where you will be empowered to sell our AI-product to C-level executives. We are customer-obsessed and constantly working to provide our customers access to actionable and insightful data to build resilient supply chains.

Benefits

Join us at [Company Name] and be part of a high-performing team that is shaping the future of the logistics and shipping industry through cutting-edge predictive analytics.

What You'll Do

As a Senior Machine Learning Engineer, your focus will be on ensuring model accuracy, reliability, and scalability in production systems.

• 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.