Job title: Machine Learning Engineer

Location: [Company location]

Reports to: [ML Engineering Manager or Head of AI]

Employment type: [Full-time]

Job summary:

We're looking for a Machine Learning Engineer to build, deploy, and maintain ML models in [product name]. The right person writes production-quality Python, understands model trade-offs, and builds systems that stay reliable at scale.

Machine learning engineers turn models into product features that customers use. Reliable ML systems improve recommendations, automation, and AI features while keeping costs and risks under control.

Key responsibilities:

• Build, train, and tune models using PyTorch, TensorFlow, or scikit-learn.
• Build data and feature pipelines with tools such as [Spark, Airflow, or dbt].
• Deploy models to production as APIs or batch jobs on [AWS SageMaker, Vertex AI, or Kubernetes].
• Build LLM features, including prompt design, retrieval-augmented generation (RAG), and evaluation.
• Set up experiment tracking and model versioning with tools such as MLflow or Weights & Biases.
• Monitor models in production for accuracy, drift, latency, and cost.
• Write tests and CI/CD pipelines for ML code and models.
• Work with data scientists to move prototypes into production.
• Check models for bias, privacy risks, and safety issues before release.
• Document model design, data sources, and limits for other teams.

• Bachelor's or master's degree in Computer Science, Machine Learning, or a related field.
• [X]+ years of experience building and deploying ML models in production.
• Strong Python skills and experience with PyTorch or TensorFlow.
• Experience with cloud platforms such as AWS, Google Cloud, or Azure.
• Experience with Docker, Kubernetes, and CI/CD.
• Solid grounding in statistics and ML fundamentals.

• Model development and evaluation.
• MLOps.
• Software engineering in Python.
• Data pipeline design.
• Debugging model and data issues.
• Clear communication of trade-offs.

• Experience fine-tuning or serving large language models.
• Experience with vector databases such as Pinecone or pgvector.
• Published research or open-source ML contributions.

• [Health, dental, and vision insurance, or private medical cover]
• [Retirement plan, such as a 401(k), pension, or superannuation]
• [Paid time off and holidays]
• [Remote or hybrid work options]
• [Conference and research budget]
• [Salary range]