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Multinational brand and Bangalore location increase applicants, but senior ML specialization moderates competition.
Deep LLM, AI engineering and cloud-native expertise required, limiting cross-industry portability.
Mandatory 10+ years, specific LLM/AWS/containerization, and leadership responsibilities enforce strict filtering.
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Lead hiring, onboarding, mentoring, and development of AI team members.
Build and maintain cloud-based AI solutions including ML serving, RAG, and AI agents integrated with data sources and core systems.
Implement observability, performance tracking, cost monitoring, and rollback mechanisms for AI systems.
10 or more years of experience in Data, Analytics & AI.
Experience with ML/AI concepts (LLM, RAG, Agentic AI, guardrails) and technologies like SageMaker, LangChain, MCP.
Strong expertise in CI/CD, containerization (Docker, Kubernetes), orchestration (Airflow), and monitoring/tracing.
Proficient in Python for cloud-native development on AWS (EKS, Lambda), API development, and relational/NoSQL databases.
Detail-oriented with a strong analytical mindset and hands-on experience in Cloud engineering and ML/AI technology.
Experienced in managing and developing AI-focused teams through hiring and mentoring.
Skilled in integrating complex AI solutions into cloud infrastructure with an emphasis on operational monitoring and cost management.