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Medium competition from metro location, mid-level experience band, and broad AI skill requirements.
Medium because AI engineering skills are transferable, but enterprise MLOps and GCP experience bias fit.
High due to explicit 5–8 year requirement plus mandatory GCP, MLOps, LLM and leadership experience.
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Define and govern AI project lifecycles including data acquisition, experimentation, production deployment, monitoring, and optimization.
Lead AI engineers and data scientists to establish best practices and drive enterprise adoption of AI using MLOps and LLMOps.
Collaborate with business and product leaders to align AI investments with measurable business outcomes and drive AI strategy across predictive analytics, Generative AI, and automation.
Bachelor’s Degree in Data Science, Machine Learning, Computer Science, Statistics, Applied Mathematics, IT, or equivalent.
5 to 8 years of experience applying AI/ML solutions and using Python-based AI/ML technologies in enterprise environments.
Experience leading AI or Data Science teams and acting as a senior technical lead for solution and architectural decisions.
Experience with Cloud AI Platforms (preferably Google Cloud Platform) and Generative AI technologies; hands-on enterprise AI deployment experience.
Proven leadership in managing and mentoring AI engineering and data science teams with ability to define AI product vision and roadmaps.
Strong technical expertise in end-to-end AI/ML systems including predictive analytics, deep learning, NLP, computer vision, retrieval-augmented generation, and agentic AI frameworks.
Experience implementing and governing Responsible AI principles including explainability, fairness, and ethical compliance, with proficiency in MLOps/LLMOps, containerization, and scalable cloud infrastructure.