<p>JOB SUMMARY</p> <p>We are seeking an AI/ML Engineer who's ready to take the wheel in shaping our next-generation AI products. You'll help architect and deploy intelligent systems that directly drive smarter decision-making across the food supply chain.</p> <p>This isn't just another ML job-it's an opportunity to apply cutting-edge machine learning techniques to real-world, high-impact problems at scale.</p> <p>Key Responsibilities:</p> <ul> <li>Build Intelligent Systems: Design, develop, and deploy ML models and pipelines that are scalable, interpretable, and optimized for real-world use cases in supply chain optimization, forecasting, and data anomaly detection. </li><li>AI-First Product Development: Collaborate with product managers, engineers, and data scientists to integrate models into customer-facing applications and services. Take projects from concept to production. </li><li>Data Engineering & Exploration: Work with massive, diverse datasets from transactional, sensor, and third-party sources. Create data pipelines and transformations using tools like BigQuery, Airflow, and Spark. </li><li>Model Experimentation & Tuning: Train and optimize classical and deep learning models using Python, TensorFlow, PyTorch, XGBoost, and Scikit-learn. Apply rigorous testing, A/B experimentation, and performance benchmarking. </li><li>Deploy at Scale: Operationalize ML models using Docker, Kubernetes, and CI/CD pipelines on GCP. Monitor drift, accuracy, and performance in production. </li><li>Own the Pipeline: Build and maintain robust MLOps workflows that enable reproducibility, model versioning, and automated retraining using MLflow or similar frameworks. </li><li>Drive Innovation: Stay ahead of the curve by evaluating emerging AI/ML technologies and integrating them into our toolchain where they create real value. </li></ul> <p>Tech Stack & Tools</p> <ul> <li>Languages & Libraries: Python, SQL, TensorFlow, PyTorch, Scikit-learn, XGBoost </li><li>Data Engineering: Oracle, BigQuery, Spark, Airflow, Pandas, dbt </li><li>MLOps & Deployment: MLflow, Docker, Kubernetes, GitHub Actions, Terraform </li><li>Cloud: GCP (BigQuery, Vertex AI, Dataflow, Cloud Functions), AWS (SageMaker) </li><li>AI Tools: GitHub Copilot, ChatGPT, Cursor AI, Otter AI </li><li>Visualization & Analysis: Looker, Plotly, Dash, Jupyter </li><li>Other: RESTful APIs, Feature Stores, Microservices Architecture </li></ul> <p>What you'll need:</p> <ul> <li>Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or equivalent hands-on experience </li><li>5+ years of experience building and deploying machine learning solutions in production </li><li>Proven ability to translate business problems into machine learning problems-and solve them </li><li>Expertise in supervised and unsupervised learning, time-series forecasting, classification, and clustering </li><li>Strong software engineering fundamentals: version control, testing, documentation </li><li>Experience working in cloud-native environments with CI/CD and containerization tools </li><li>Ability to explain complex ML concepts to technical and non-technical audiences </li><li>Experience with real-time inference, large-scale batch predictions, and model retraining workflows </li></ul> <p>Nice-to-Have Qualifications:</p> <ul> <li>Bonus: Knowledge of optimization, recommendation systems, LLMs, or graph neural networks </li></ul> <p>If you are a highly motivated and results-driven individual, with a passion for driving growth in a fast-paced, entrepreneurial environment, we encourage you to apply for this exciting opportunity. We offer a competitive salary, comprehensive benefits package, and a dynamic work culture that values collaboration, innovation, and personal development.</p>