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Popular generalist ML role, metro location, and broad required skillset increase applicant competition.
Core ML engineering, deployment, and MLOps skills are highly transferable across industries.
Multiple mandatory technical stacks, production MLOps and deployment requirements will create strict filtering.
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Design, build, train, and optimize machine learning and deep learning models, including researching and prototyping new algorithms.
Deploy models to production ensuring scalability, performance, and maintainability, including building APIs and automation pipelines.
Collaborate cross-functionally to translate business needs into technical AI/ML solutions and monitor model performance post-deployment for retraining as needed.
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or related field.
Strong programming skills in Python with experience in ML libraries such as TensorFlow, PyTorch, and scikit-learn.
Experience with data handling tools like SQL, Pandas, Spark and cloud platforms (AWS, Azure, or GCP) for deploying models at scale.
Knowledge of MLOps practices including CI/CD for ML, model versioning, monitoring tools, and familiarity with REST APIs, microservices, containerization (Docker, Kubernetes).
Experienced AI/ML engineer with end-to-end model development and production deployment expertise.
Proficient in operating within cloud environments and implementing MLOps practices to ensure reliable model delivery.
Comfortable working cross-functionally to align AI/ML solutions directly with business impact and product workflows.