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Metro Bangalore, desirable ML/MLOps skills and broad remit increase competition despite lesser-known employer.
Core ML/MLOps skills are transferable, but insurance domain and RPA specificity increase sensitivity.
Requires specialized ML, MLOps, architecture, and governance expertise, so screening will be strict.
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Own design and development of complex AI and automation solutions across policy and claims domains, including reusable accelerators and non-functional production-grade requirements.
Drive automation strategy and process analysis with stakeholders, prioritizing pipeline projects by value, feasibility, and strategic fit, and lead discovery in ambiguous problem spaces.
Architect and oversee AI/ML model development, MLOps standards, and responsible AI practices; lead integration architecture and define engineering and governance standards for automation practice.
Experience required: Not explicitly mentioned in the JD.
Strong expertise in AI/automation solution design including RPA, API, AI integrations, and ML/NLP model development.
Proven skills in MLOps, production-grade engineering (performance, HA/DR, scalability), and responsible AI practices (bias, auditability).
Experience in integration architecture, API strategy, and automation governance including CI/CD, testing, and incident response.
Candidate thrives in technically complex environments requiring hands-on ownership of AI and automation across multiple insurance domains.
Demonstrates strategic influence in automation roadmap planning and process standardization aligned with business value and operational impacts.
Experienced in defining and implementing production engineering standards and governance for scalable, resilient automation systems with cross-team collaboration.