Machine Learning Engineer / AI Engineer

Overview

This role owns the design, deployment, and long-term performance of production machine learning systems that power data‑driven products and operational intelligence.

The Machine Learning Engineer is responsible for translating business problems into scalable AI solutions. This includes building end‑to‑end pipelines, deploying models into production environments, and continuously improving model performance.

This is an engineering execution role, not a research‑only position. Success requires the ability to move models from experimentation to reliable production systems that drive measurable product and operational impact.

Requirements

• Experience building and deploying machine learning models in real‑world production environments
• Strong Python programming capability and experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit‑learn
• Understanding of statistics, probability, and optimization techniques used in machine learning systems
• Experience working with large‑scale structured and unstructured datasets
• Experience deploying and operating models on cloud platforms such as AWS, GCP, or Azure
• Ability to build and manage scalable data pipelines supporting ML workflows
• Strong collaboration skills working with product, engineering, and data teams

Responsibilities

Machine Learning Development

• Design, train, and optimize machine learning models for real‑world business applications
• Build end‑to‑end ML pipelines including data ingestion, preprocessing, training, and evaluation
• Select appropriate algorithms and modeling approaches based on problem context
• Continuously improve model performance through experimentation and iteration

Production Deployment & Infrastructure

• Deploy machine learning models into scalable production environments
• Build APIs and services that operationalize machine learning capabilities inside products and systems
• Manage the full model lifecycle including monitoring, retraining, and drift detection
• Ensure reliability, scalability, and maintainability of ML infrastructure

Data & Engineering Execution

• Work with structured and unstructured data sources to support model development
• Implement feature engineering strategies that improve model accuracy and reliability
• Collaborate with data engineering teams to maintain clean and scalable data infrastructure
• Optimize model performance and resource efficiency in production systems

Cross‑Functional Collaboration

• Partner with product and engineering teams to translate business needs into machine learning solutions
• Communicate technical insights and model behavior to non‑technical stakeholders
• Contribute to AI roadmap planning and prioritization of ML initiatives

Must‑Haves

• Proven experience deploying machine learning models into production environments
• Strong Python and ML framework expertise (TensorFlow, PyTorch, or Scikit‑learn)
• Experience building scalable ML pipelines and APIs
• Experience working with cloud infrastructure (AWS, GCP, or Azure)
• Strong understanding of statistical modeling and machine learning fundamentals

Invitation to Apply

If you are a hands‑on machine learning engineer who thrives on building real‑world AI systems and taking models from concept to production impact, this is an opportunity to own meaningful technology that drives product innovation and operational performance.