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Strong employer brand, metro location, and broad AI/ML/full-stack requirements increase applicant competition.
Applied ML, MLOps, and integration skills transfer well, but regulated life-sciences experience raises specificity.
Explicit 8–13 years requirement plus mandatory production AI, MLOps, and technical ownership raises filter strictness.
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Lead end-to-end technical delivery of complex AI and automation solutions across discovery, design, build, production deployment, stabilization, and measurable business impact.
Translate complex business challenges into executable AI product designs, coordinate multidisciplinary teams to deliver solutions, and ensure production readiness including monitoring, rollback, and operational support.
Own integrated architecture, testing, evaluation, governance, and production operations encompassing AI/ML models, APIs, workflows, security, and compliance.
Bachelor’s or Master’s degree in Computer Science, IT, or related field.
8 to 13 years of professional experience.
Demonstrated end-to-end technical ownership of at least one production AI, ML, software, data, or automation solution with measurable enterprise impact.
Strong hands-on proficiency in Python and SQL; experience with production software, APIs, and enterprise integrations.
Experienced in complex AI/ML enterprise solution architecture including GenAI, RAG, agents, and MLOps/LLMOps operations.
Capable of coordinating cross-functional teams to deliver AI solutions from discovery through production with strong technical leadership and problem-solving skills.
Comfortable working in regulated environments like life sciences or healthcare with attention to compliance, governance, and quality controls.