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Recognizable employer and metro location increase applicant density, but senior AI lead reduces overall competition.
Low because core AI/ML engineering skills and MLOps are highly transferable across industries.
High due to mandatory deep ML/GenAI, MLOps, cloud (Azure) and production-delivery experience requirements.
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Design, develop, and deploy AI and machine learning models, including NLP and GenAI use cases, aligned with product goals and business requirements.
Integrate AI capabilities into product features and workflows ensuring scalable, reliable deployment in collaboration with engineering teams.
Manage full ML model lifecycle including data pipelines, training, evaluation, deployment, monitoring, and performance tuning while ensuring compliance with risk and responsible AI standards.
Strong experience with Python and AI/ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.
Hands-on experience with Generative AI / Large Language Models including prompt engineering and RAG architectures.
Familiarity with MLOps practices, cloud platforms (Azure preferred; AWS/GCP acceptable), and data engineering concepts (SQL, data pipelines, APIs).
Work Experience Required: Proven track record of delivering AI solutions in production environments; specific years not mentioned.
Experienced in agile, squad-based product delivery models with cross-functional collaboration among business, product, and engineering teams.
Demonstrates ability to translate complex business problems into scalable AI/ML solutions with measurable impact.
Strong focus on continuous improvement and governance including documentation, security, privacy, and responsible AI usage.