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Mid-level popular AI role with broad skill requirements and moderate employer brand, driving medium competition.
Core ML engineering skills are transferable, but domain-specific production and governance expectations require moderate industry fit.
Explicit 3–5 year requirement plus mandatory production ML experience, specific frameworks, and deployment readiness increase strictness.
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Design, train, and optimize robust AI/ML models using frameworks like Kubeflow, PyTorch, TensorFlow, or HuggingFace with focus on explainability, scalability, and resource efficiency.
Translate business use cases into scalable AI model architectures, collaborating with Data Engineers for deployment integration and readiness.
Document model assumptions and architecture; support scoping and delivery of AI features within data product initiatives while mentoring junior AI engineers.
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or related field.
3–5 years of experience designing and implementing AI/ML models in production.
Strong Python development skills with experience in ML/DL libraries (Kubeflow, PyTorch, TensorFlow, scikit-learn).
Experience with AI use cases like agents, vision, or forecasting; understanding of model lifecycle including versioning and monitoring.
Experienced in advanced AI/ML techniques such as deep learning, NLP, computer vision, and classical ML with an emphasis on model explainability and compliance.
Skilled at translating complex business requirements into scalable AI solutions and collaborating across cross-functional and distributed teams.
Proficient in documenting and standardizing model development processes with ability to mentor juniors and contribute to AI team capability building.