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Mid-level project title but specialized GenAI/MLOps skills and cloud experience increase applicant competition.
Specialized GenAI and MLOps expertise transfers across AI-product companies but less to non-technical industries.
Role mandates specific GenAI, MLOps, cloud, and infra skills, creating strict technical screening filters.
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Lead architecture and system design for AI infrastructure including LLM orchestration, vector databases, and cloud compute.
Manage Agile/Scrum processes for multidisciplinary teams comprising data scientists, MLOps, and software engineers.
Establish code and pipeline guardrails, monitor resource usage and optimize performance and cost metrics such as API latency and token usage.
Proficiency in Python, PostgreSQL, AI & Machine Learning, Generative AI, with familiarity in Node.js, AWS/GCP, PGSQL/MongoDB, Azure AI Foundry.
Experience leading Agile/Scrum teams in technical environments.
Work Experience Required: Not explicitly mentioned in the JD.
Ability to design and audit AI model performance including monitoring for model drift, bias, and technical debt.
Experienced technical lead capable of managing cross-functional AI and software development teams.
Strong architectural background in AI infrastructure and cloud technologies.
Detail-oriented with a focus on maintaining quality code, CI/CD pipelines, and optimizing AI system performance and cost.