Machine Learning Engineer AIRLKLHV

Job Title: Machine Learning Engineer

Location: USA / Canada (Remote / Hybrid)
Job Type: Contract / Full-Time (Based on Engagement)

Overview

We are seeking a skilled Machine Learning Engineer to develop deploy and maintain machine learning models that power business insights and automation. This role bridges the gap between data science and production systems ensuring models are scalable reliable and deliver measurable impact.

The ideal candidate has hands-on experience across the full machine learning lifecycle from model development to deployment and monitoring along with strong collaboration skills to integrate ML solutions into enterprise systems.

Responsibilities

• Develop and deploy machine learning models for diverse business use cases
• Collaborate with data scientists to transition models from research to production
• Build and maintain data pipelines to support ML workflows
• Optimize models for performance scalability and accuracy
• Implement monitoring alerting and maintenance processes for deployed models
• Integrate machine learning capabilities into enterprise applications
• Ensure data quality and integrity for training and evaluation
• Stay current with emerging ML technologies and best practices

Required Qualifications

• Strong experience in machine learning model development and deployment
• Proficiency in Python and ML frameworks (e.g. TensorFlow PyTorch or similar)
• Experience with data engineering and pipeline development
• Familiarity with cloud-based ML platforms (AWS Azure or GCP)
• Solid understanding of statistical modeling and data analysis
• Experience working in enterprise environments
• Strong problem-solving and analytical skills

Must-Have Skills

• Proven experience deploying production-grade ML models with measurable business impact
• Hands-on experience with MLOps pipelines model monitoring and lifecycle management
• Ability to build scalable and reliable ML systems
• Strong collaboration and communication skills

Preferred Qualifications

• Experience with containerization and orchestration (Docker Kubernetes)
• Familiarity with CI/CD pipelines for ML workflows
• Knowledge of feature engineering model versioning and experiment tracking tools
• Exposure to real-time or streaming ML applications

Work Environment

• Remote / Hybrid flexibility
• Fast-paced collaborative and innovation-driven team

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