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Tier-1 brand, metro Bengaluru location, and broad ML/LLM plus multi-cloud skills increase competition.
Specialized ML/LLM and MLOps skills moderately restrict cross-industry transferability.
Explicit 5+/6+/7+ seniority plus mandatory ML, LLM, multi-cloud and MLOps skills enforce strict shortlisting.
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Design, build, deploy, and scale machine learning and generative AI systems for production across multi-cloud environments (GCP, AWS, Azure).
Develop end-to-end ML pipelines including data ingestion, feature engineering, training, deployment, and monitoring with focus on robust, scalable, cost-efficient systems.
Collaborate with AI leadership and business teams to deliver production-grade LLM and agent-based solutions integrating multiple cloud services, maintaining MLOps best practices.
PhD with 5+ years, Master’s with 6+ years, or Bachelor’s with 7+ years in Machine Learning, Computer Science, Data Science, or related field.
Strong proficiency in Python and experience building production-grade ML systems with at least one major cloud platform (GCP, Azure, or AWS).
Hands-on experience with deep learning frameworks (PyTorch, TensorFlow) and container technologies (Docker, Kubernetes).
Work Experience Required: Minimum 5 years relevant experience based on highest degree obtained. Notice Period: Not explicitly mentioned in the JD.
Experienced in operationalizing ML/LLM solutions emphasizing scalable, reliable production systems rather than purely research-focused work.
Skilled in multi-cloud engineering and MLOps best practices including CI/CD, model versioning, and automated retraining.
Comfortable collaborating across business and AI leadership teams to translate advanced AI concepts into integrated, agent-based applications within cloud ecosystems.