Senior Engineer, AI Tech Services, India
On-siteBengaluru, Karnataka, India
Job Summary
Design complex, cross-system automation programs spanning policy and claims domains using RPA, API, and AI. Architect ML/NLP solutions for classification and fraud detection while establishing MLOps standards for deployment and drift detection. Lead integration architecture across core platforms, defining API strategies and event models to ensure scalability for high-volume automation. Drive automation roadmaps with stakeholders to prioritize pipelines based on value and feasibility, establishing ROI measurement methodologies. Define engineering standards for the practice, including code quality, CI/CD, and observability, while mentoring engineers and providing technical direction. Collaborate with platform teams on identity and network strategy to ensure production readiness.
Required Qualifications
- Own solution design for complex, cross-system automation programs spanning policy and claims domains
- Establish patterns and reference architectures for RPA + API + AI implementations (resiliency, observability, scalability, governance)
- Lead development of reusable accelerators for insurance workflows (intake, triage, endorsements, payments, document intelligence)
- Define non-functional requirements and ensure production-grade engineering: performance, HA/DR considerations, maintainability, and operational supportability
- Guide technical tradeoffs across RPA vs. workflow engines vs. microservices to minimize brittleness and improve long-term scalability
- Drive automation roadmaps with stakeholders: prioritize pipeline based on value, feasibility, risk, and strategic fit
- Establish ROI measurement methodologies and benefits tracking; ensure realized value aligns with business cases
- Lead discovery for ambiguous problem spaces; align stakeholders on target state, operating model, and change impacts
- Influence process standardization and redesign to maximize automation leverage (not just lift and shift)
- Architect and oversee ML/NLP solutions for classification, extraction, and fraud/anomaly detection; ensure appropriate evaluation and validation rigor
- Define MLOps standards (deployment, monitoring, drift detection, retraining triggers, model governance) and operational ownership
- Ensure responsible AI practices: bias considerations, explainability where required, auditability, and model risk controls
- Lead integration architecture across core platforms and enterprise services; define API strategies, event models, and data contracts
- Ensure scalability for high-volume automation (load management, horizontal scaling, queue-based designs, back-pressure handling)
- Coordinate with platform/security teams on identity, network, environment strategy, and production readiness reviews
- Define engineering standards for the automation practice (code quality, CI/CD, testing, observability, incident response)
- Establish KPI frameworks and executive-ready reporting for automation health and value realization
- Drive reduction of operational risk through governance, controls, and audit-ready documentation
- Mentor and review work from other engineers; provide technical direction without becoming a bottleneck
- Foster a respectful and collaborative environment by upholding professionalism and engaging in constructive and respectful conversations
- Support a positive workplace culture by promoting open, solution-oriented communication, encouraging teamwork, and maintaining a focus on shared goals and productivity
- Willingness to learn and develop skills
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