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Tier-1 brand, metro location, and broad multidisciplinary AI/backend requirements increase applicant competition.
Requires deep financial domain and regulatory expertise, so candidate backgrounds must closely match.
Mandatory 9–12 years, Java/Spring, ML/LLM and regulated finance experience creates strict shortlisting filters.
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Lead the safe, reliable, and explainable application of AI in business-critical credit risk platforms, defining AI strategies and integration with business workflows.
Design and enforce guardrails, validation patterns, and orchestration layers ensuring AI outputs align with business rules, compliance, and auditability.
Drive drift detection frameworks, production readiness standards, and provide technical leadership and mentorship to engineering teams building AI-augmented services.
9–12 years of hands-on software engineering experience with strong expertise in Java and Spring / Spring Boot ecosystem.
Experience with distributed, high-throughput, low-latency backend services, microservices architecture, REST APIs, event-driven systems (Kafka or similar), databases, containerization, CI/CD, and cloud platforms (AWS / Azure / GCP).
Practical knowledge of AI/LLM concepts, AI output guardrails, model/prompt drift detection, and orchestration frameworks such as LangChain or equivalent.
Work Experience Required: 9–12 years software engineering in relevant domains (financial services including counterparty risk or security services). Location: Pune, India.
Technical leader capable of bridging business stakeholders, risk teams, and engineering to deliver compliant, production-grade AI solutions in financial services.
Experienced in embedding explainability, trust, and operational discipline into AI systems with strong focus on regulatory and risk frameworks.
Skilled in mentoring engineers and influencing architectural decisions with a pragmatic, business-aligned approach to AI integration and continuous production support.