Goodleap

Senior Machine Learning Engineer

Job Description

Position Summary

Senior Machine Learning Engineer will work closely with engineering tech leads to deploy LLM models into production, build scalable ML infrastructure, and optimize ML workflows. This role will play a crucial role in defining and scaling ML/AI applications in production, ensuring efficiency, reliability, and automation across model development, training, and evaluation.




Essential Job Duties and Responsibilities
  • Collaborate with engineering tech leads to integrate LLM models into production systems.
  • Identify and solve engineering pain points by building scalable, general-use ML platforms.
  • Design and develop scalable infrastructure and pipelines for data/feature processing, model training, and evaluation.
  • Automate ML workflows to improve productivity across training, evaluation, testing, and results generation.
  • Partner with cross-functional teams to define the long-term vision for ML/AI applications and contribute to roadmap planning.
  • Implement ML Ops best practices, ensuring efficient model deployment, monitoring, and versioning.
  • Optimize and manage distributed processing architectures using Spark, Databricks, Airflow, Kubeflow, MLflow, etc.
  • Develop microservices-based architectures for ML applications, including RESTful APIs for model serving.
  • Ensure compliance with scalability, reliability, and security standards in ML production systems.


    • Required Skills, Knowledge, and Abilities
  • Master’s degree in computer science, Machine Learning, or a related field with 5+ years of experience as an ML Engineer or ML Scientist in an industry setting.
  • Strong programming skills in Java, Python, and SQL/MySQL.
  • Hands-on experience in ML Ops, including large-scale ML applications, services, pipelines, and architectures.
  • Solid understanding of system design for ML systems, including design patterns, OOD (Object-Oriented Design), and interface design.
  • Experience with distributed processing architectures and ML/data workflow management platforms (e.g., Spark, Databricks, Airflow, Kubeflow, MLflow).
  • Experience with containerization and orchestration tools like Docker and Kubernetes

    • Preferred Qualifications
  • Ph.D. in Computer Science, Machine Learning, or a related field, or 3+ years of ML Engineering experience in addition to a Master’s degree.
  • Strong theoretical and practical understanding of machine learning models and frameworks (Scikit-Learn, TensorFlow, PyTorch, etc.).
  • Experience working with cloud-based solutions, especially AWS and Databricks.
  • Experience with CI/CD pipelines, automated testing, and test-driven development for ML applications.
  • Knowledge of microservice architectures and best practices for RESTful web services.


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