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Strong employer, mid-senior ML/AI role, metro Bangalore, and generalist LLM/MLOps skillset increase candidate competition.
Core ML/LLM engineering skills transfer across industries, though regulated-pharma experience is a desirable, not mandatory, differentiator.
Explicit 7–10 years plus mandatory production ML, LLM, cloud, and MLOps expertise makes screening highly selective.
Build, test, and deploy production-grade AI/ML solutions including generative AI/LLMs with measurable business impact.
Own end-to-end technical delivery, including hands-on coding, productionization with MLOps, and architecture optimization for scalable, secure AI systems.
Collaborate cross-functionally to integrate AI workflows with data, engineering, and product teams ensuring reliability, maintainability, and operational governance.
7–10 years in software engineering, machine learning engineering, AI engineering, data science engineering or closely related technical field.
Proven experience building and deploying production AI/ML applications, not just leading projects or teams.
Strong programming skills in Python and experience with ML frameworks like PyTorch, TensorFlow, or scikit-learn.
Experience with generative AI/LLM applications and deploying AI/ML solutions in at least one major cloud (Azure, AWS, or GCP).
Experienced in hands-on delivery of AI/ML systems from prototype through production with strong ownership of technical workstreams.
Skilled at engineering scalable, secure AI architectures and integrating complex data pipelines and cloud infrastructure.
Experienced working collaboratively with diverse teams (data scientists, engineers, product) in regulated or enterprise environments with responsible AI practices.