Machine Learning (ML) Engineer

Key Responsibilities

• Define ML Problems & Metrics – Work with product and engineering teams to clearly scope machine learning problems and measure success with appropriate metrics.
• Train & Evaluate Models – Build, train, validate, and improve models for classification, prediction, NLP, or computer vision tasks with quality results.
• Build Data Pipelines – Design and maintain efficient data pipelines for model training and inference using modern ETL practices.
• Deploy to Production – Deploy trained models into live systems and monitor performance, reliability, and drift in production environments.
• Collaborate with Teams – Work closely with engineers, data scientists, and product owners to integrate ML outputs into business applications.
• Optimize & Tune Models – Continuously refine models, features, and algorithms to improve performance and scalability.
• Document & Communicate – Write clear documentation for models, experiments, and results; communicate insights to technical and non‑technical stakeholders.

Requirements

• Experience – 3+ years in ML engineering, data science, or related roles with deployed projects.
• Education – Bachelor’s in Computer Science, Engineering, or similar technical field.
• Technical Competence – Strong Python skills; experience with TensorFlow, PyTorch, or similar ML frameworks.
• Project Controls – Proven ability to manage ML project lifecycles from design to monitoring post‑deployment.
• Leadership – Ability to guide junior team members and contribute to best practices in model development.
• Commercial Acumen – Understand business priorities and frame ML solutions that deliver measurable value.