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Strong global pharma brand and metro location increase competition, though senior ML manager specialization narrows candidate pool.
ML and MLOps skills transfer across industries, but pharma context and leadership needs moderately favor domain experience.
Explicit 7–11 years, mandated AI/management experience, and specific ML/MLOps tech stack create strict shortlisting filters.
Manage and develop a team of 3-4 AI, cloud, and full stack engineers delivering scalable AI solutions using cloud-native platforms (primarily AWS, with GCP as a plus).
Oversee design, deployment, and operation of robust AI systems including backend, frontend, and AI platform components ensuring scalability, reliability, and security.
Collaborate with global stakeholders, product managers, data scientists, and architects to design and implement AI engineering projects, driving best practices in software engineering, MLOps, and DevOps.
7-11 years of software engineering experience including at least 3 years in AI engineering and 3 years in people management.
Bachelor’s degree in Information Technology, Computer Science, or related Technology field.
Strong hands-on Python programming skills and experience with cloud-native solutions on AWS; exposure to GCP is a plus.
Past international job experience and ability to work in diverse multicultural environments.
Experienced leader able to mentor engineers and manage a small team focused on AI and cloud engineering within large-scale, globally distributed technology centers.
Proficient in modern DevOps practices (CI/CD, containerization, infrastructure as code) and comfortable driving engineering best practices in a hybrid work environment.
Skilled in collaborating across multi-disciplinary global teams including product management, data science, and architecture to deliver business-impacting AI solutions.