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Mid-level ML role with broad skillset, metro location, and reputable employer, creating high competition.
Core ML and MLOps skills are broadly transferable, though life-sciences experience is a beneficial differentiator.
Explicit 5–8 years plus mandatory MLOps, AWS, and production ML requirements create strict shortlisting filters.
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Design, develop, deploy, and monitor scalable predictive machine learning models and pipelines, including time-series forecasting and classification.
Implement MLOps practices for model versioning, experiment tracking, automated testing, CI/CD, and reproducible deployment primarily on AWS.
Develop and maintain APIs and services for ML models, ensure model performance monitoring, retraining automation, and support production ML workloads including Generative AI and LLM applications.
5–8 years of experience in Software Engineering, Data Science, or Machine Learning Engineering.
Strong hands-on experience with Python, machine learning libraries (Scikit-learn, PyTorch, TensorFlow, XGBoost), and AWS cloud services (SageMaker, S3, Lambda, etc.).
Proven experience with MLOps pipelines, including model deployment, monitoring, versioning, and retraining.
Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field.
Experienced in building and operationalizing machine learning models with strong expertise in time-series forecasting and predictive modeling.
Skilled in cloud-native ML deployments, especially on AWS, with solid understanding of MLOps best practices and production-grade API development.
Capable of collaborating cross-functionally with data scientists, engineers, and product teams to deliver scalable and secure ML solutions including emerging fields like Generative AI and LLM applications.