Machine Learning Engineer / AI Engineer

Mai PlacementNewark, NJ
5h$120,000 - $180,000Onsite

About The Position

We are seeking a highly skilled Machine Learning Engineer to design, build, and deploy intelligent systems that power next-generation AI-driven products. This role is ideal for a hands-on builder who understands both the science and the engineering behind machine learning. You will work closely with product, engineering, and leadership teams to turn business problems into scalable AI solutions. You won’t just experiment — you will ship production-ready models.

Requirements

  • 3+ years of hands-on machine learning engineering experience
  • Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn)
  • Experience deploying ML models into production environments
  • Solid understanding of statistics, probability, and optimization techniques
  • Experience with cloud platforms (AWS, GCP, or Azure)
  • Experience building data pipelines and working with large datasets

Nice To Haves

  • Experience with NLP, LLMs, or generative AI (e.g., ChatGPT-style systems)
  • Experience fine-tuning or integrating large language models
  • Background in AI product development within SaaS environments
  • Experience with MLOps and CI/CD pipelines
  • Experience working in fast-paced startup environments

Responsibilities

  • Design, train, and optimize machine learning models for real-world applications
  • Deploy models into scalable production environments
  • Build pipelines for data ingestion, preprocessing, training, and evaluation
  • Monitor model performance and iterate based on real-world usage
  • Work with structured and unstructured data sets
  • Implement best practices for feature engineering and model selection
  • Optimize model accuracy, performance, and reliability
  • Collaborate with data engineers to ensure clean and accessible datasets
  • Build robust APIs and services around ML models
  • Ensure systems are scalable, secure, and maintainable
  • Balance experimentation with production stability
  • Address model drift and long-term model lifecycle management
  • Partner with product and engineering teams to define requirements
  • Translate business objectives into ML strategies
  • Communicate technical findings clearly to non-technical stakeholders
  • Contribute to roadmap planning for AI-driven features
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