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Tier-1 brand, mid-level ML title, metro location, and broad GenAI/MLOps scope increase candidate competition.
ML skills are transferable, but regulated biotech/GxP context raises domain specificity and hiring preference.
Explicit 5–9 years requirement, mandatory ML production ownership and specific tech/MLOps skills make shortlisting strict.
Independently own and manage defined production components of enterprise AI products including models, APIs, data pipelines, or evaluation modules.
Design, implement, release, diagnose, and support maintainable Python, SQL, API, data, model, retrieval, agent-tool, and workflow components with measurable business impact.
Apply machine learning, GenAI, RAG, and MLOps practices while ensuring security, privacy, Responsible AI compliance, and regulated delivery controls.
5 to 9 years of experience in Computer Science, IT, or related field.
Bachelor’s or Master’s degree in Computer Science, IT or related field.
Proficiency in Python and SQL with software engineering and testing experience.
Demonstrable ownership of at least one production software, data, ML, GenAI, or automation component.
Experienced in production AI/ML system design with strong technical judgment from design through production support.
Proficient in at least one core area: classical ML, GenAI/RAG/agents, or MLOps/platform engineering, with working knowledge in adjacent fields.
Comfortable collaborating across product, architecture, data, platform, and control teams in regulated, high-impact environments such as healthcare or life sciences.