Machine Learning Engineer

Role Overview

We are looking for a Machine Learning Engineer who is excited to apply data science and AI techniques to real business problems. In this role, you will support the design, development, and deployment of machine learning solutions under the guidance of senior engineers and data scientists, working closely with product and business teams to turn data into practical, impactful products.

Core Responsibilities

• Assist in designing and implementing machine learning models for use cases such as pricing, dispatch, personalization, forecasting, recommendation, or computer vision
• Perform exploratory data analysis (EDA), contribute to feature engineering, and help maintain data preprocessing pipelines in collaboration with data engineers
• Support prototyping and experimentation with algorithms, running experiments and evaluating model performance using defined business metrics
• Write clean, well-structured Python code for model training, evaluation, and basic API or batch integration, following team best practices
• Help monitor model performance in production, investigate issues such as data or model drift, and contribute to retraining or improvement workflows
• Work with MLOps and software engineering teams to integrate models into production systems and document model behavior and usage
• Communicate analysis results and model insights clearly to teammates and non-technical stakeholders, using appropriate visualizations and simple explanations

Required Skills

• Bachelor’s Degree in computer science, Statistics, Mathematics, Engineering, or a related quantitative field; a relevant Master’s degree is a plus but not required
• Minimum 1-2 years of hands-on experience in Data Science, Machine Learning Engineering, or AI Engineering (including internships or industry projects)
• Solid understanding of core machine learning concepts and algorithms (e.g., regression, classification, clustering, basic neural networks) and statistical fundamentals
• Proficiency in Python for data analysis and modeling, with experience using libraries such as Pandas, NumPy, and Scikit-learn; basic familiarity with visualization tools (e.g., Matplotlib, Seaborn)
• Exposure to at least one deep learning framework (e.g., TensorFlow or PyTorch) through coursework, projects, or professional work
• Practical experience writing SQL and working with datasets from data warehouses or data lakes
• Basic understanding of software engineering practices, including version control (Git), code reviews, and writing readable, maintainable code
• Exposure to MLOps or ML lifecycle tools (e.g., MLflow, DVC, Kubeflow, Vertex AI, SageMaker) is a plus but not mandatory
• Familiarity or interest in Generative AI models and frameworks (e.g., LangChain, LlamaIndex, RAG pipelines) is a strong plus
• Any experience in high-velocity domains such as ride-hailing, logistics, e-commerce, or pricing optimization is an advantage, but not a requirement.

What We Are Not Looking For

To avoid mismatches, we do not target candidates who mainly specialize in:

• Pure academic ML research without production deployment experience
• Pure data engineering without ownership of model building and impact
• MLOps roles focused on infrastructure but not model design
• Data analysts without hands-on ML model development experience
• LLM application roles without broader ML lifecycle experience