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Tier-1 brand, metro location, and a visible ML/AI manager role drive high candidate competition.
Generative AI and MLOps skills transfer across industries, but pharma regulatory and commercial analytics experience raise domain specificity.
Explicit 6+ years plus mandatory generative AI, RAG, MLOps, and production deployment skills increases filtering strictness.
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Lead end-to-end technical design, build, and deployment of AI solutions improving commercial channel investments.
Build and maintain agentic AI systems including multi-agent orchestration and integration with business platforms.
Develop and manage RAG architectures, data pipelines, and MLOps practices ensuring production-grade, compliant AI deployments.
Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or related technical field; Master's preferred but equivalent hands-on AI/ML expertise accepted.
6+ years of progressive hands-on experience in software engineering, data science, AI/ML, or data engineering with proven production system delivery.
Hands-on expertise in Generative AI, RAG architectures, agentic AI frameworks, Python coding, SQL, and cloud AI platforms (Azure, AWS, or GCP).
Experience building and maintaining AI data pipelines and familiarity with MLOps deployment and monitoring practices.
Deep practitioner comfortable with both high-level architecture and detailed coding in AI/ML production environments.
Experienced in commercial analytics or regulated industries with understanding of compliance and AI governance.
Skilled in integrating advanced AI technologies (multi-agent frameworks, vector DBs, LLM tuning) with business workflows and stakeholders.