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Tier-1 brand, metro location, mid-level GenAI role, and broad tech requirements increase competition.
Requires specialized GenAI, LLM, and MLOps expertise, limiting easy industry transfer.
Explicit years, mandatory GenAI/ML, cloud and MLOps tech make filters stringent.
Identify, prototype, and implement practical AI and Generative AI solutions (including RAG pipelines and multi-agent systems) to automate workflows and deliver measurable business value in service operation units.
Design, develop, and harden production-grade AI microservices and agent-based applications leveraging cloud platforms (Azure/AWS), container orchestration, and API frameworks.
Lead technical aspects of AI governance including responsible AI implementation, security, monitoring, and continuous improvement of deployed solutions.
Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or equivalent experience.
4+ years of experience owning AI/ML domain-specific strategy, governance, delivery, and stakeholder management.
4-6 years delivering AI/ML, Data Science solutions in production environments, including 2-3 years focused on Generative AI/LLM applications.
Proficient in Python programming, AI architecture design, development of RAG pipelines, agent frameworks, and cloud technologies (Azure/AWS), with knowledge of MLOps tools (Docker, Kubernetes, CI/CD).
Experienced in delivering enterprise-scale AI/ML digital solutions with strategic ownership of AI solution lifecycle in cross-functional, global teams.
Strong expertise in Generative AI, LLM integration, prompt engineering, and agent orchestration frameworks relevant to service operations.
Hands-on with cloud-native AI deployments, microservices architecture, security governance (Responsible AI, data privacy), and operational metrics to ensure scalability and reliability.