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Tier-1 employer but senior ML specialization yields moderate applicant competition.
Core ML, MLOps, and cloud skills are broadly transferable across industries.
Explicit 8+ years, 3+ ML years, and many mandatory ML/MLOps/cloud skills indicate high filtering.
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Own end-to-end machine learning and generative AI projects, including conception, development, and operationalization within an agile product model.
Set technical direction, provide vision and guidance to team members, and raise engineering quality standards.
Design and implement scalable AI applications leveraging prediction and optimization models to drive significant business impact for corporate functions.
Undergraduate degree in Computer Science or Master's in a related engineering field, or equivalent experience.
8+ years of professional software engineering experience, including 3+ years in machine learning engineering or related fields.
Proficiency in Python, AWS (including SageMaker, Lambda, API Gateway), and database technologies like Postgres and Redis.
Demonstrable history of technical leadership, mentoring, and delivering value in agile product environments.
Experienced technical leader comfortable influencing strategy and mentoring engineers in AI/ML engineering contexts.
Skilled in developing and deploying predictive and optimization models in production using MLOps practices.
Proficient working with cloud architectures (especially AWS), containerization (Docker, Kubernetes), and data engineering tools suitable for large-scale ML systems.