Senior Machine Learning Engineer

Salary Range: 137000 to 165000 (Currency: USD) (Pay period: per-year-salary)

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.