Newgens AI-first low-code platform powers end-to-end banking journeysdigital account opening, retail & commercial lending, transaction banking (trade & supply chain finance), and collections. Your charter is to lead the engineering of production-grade AI/GenAI and agentic capabilities that deliver measurable improvements in speed, accuracy, compliance, and cost for these journeys.

Key Outcomes & Engineering Responsibilities :

- Define the 12 to 24-month Banking AI engineering roadmap across digital onboarding, RLOS/CLOS, trade finance, collections, and wealthprioritized by business outcomes (TAT, first-time-right, risk, recovery).

- Architect multi-tenant AI services (LLM/RAG, decisioning, forecasting) with API-first interfaces consumable by Newgen banking apps and partner ecosystems and customers including authentication, tenancy isolation, rate-limiting, and observability.

- Build decisioning & risk services : credit score augmentation, policy/DMN alignment, explainability, and Early Warning Signals integrated with LOS/CLOS and servicing workflows.

- Ship document intelligence for banking : GenAI summarization/extraction/redaction for onboarding & loans; UCP-600 compliant trade checks (document classification, discrepancy detection, cross-doc validation) with auditable trails.

- Operationalize agentic automations for underwriting, financial spreading, KYC/AML and exception handling with evaluated guardrails (input/output filters, grounding, policy rules).

- Deliver Collections AI : including data and voice.

- Establish Banking-grade MLOps/LLMOps : data contracts, model & embedding registries, offline/online evaluation (latency, cost, grounding, bias), CI/CD.

- Own non-functional SLOs : availability, latency, cost/1k tokens, throughput, and error budgets; institute golden-path SDKs and reference adapters for rapid solution team adoption. (Platform practice)

- Partner with Product, Compliance, and Security to codify AI governance : PII handling/redaction, auditability, retention, model/prompt guardrails, and regulator-ready documentation. (Governance practice)

- Lead and mentor a high-performing engineering team (applied scientists, ML engineers, platform engineers, evaluators); build hiring plans and coach for design rigor, reliability, and impact.

Qualifications :

- 10 to 12 years total experience; 5+ years leading AI/ML engineering teams shipping production AI in banking (retail/commercial lending, onboarding/KYC, trade operations, collections).

- Strong systems design : multi-tenant AI services, distributed event architectures, streaming ETL, vector/feature stores, observability and cost/performance optimization.

- LLMs & decisioning depth : prompting, fine-tuning, RAG; explainable decisioning aligned to credit policy/DMN; time-series EWS and risk models.

- Document AI for banking : OCR/ICR + layout models; trade document checks against UCP-600; KYC/AML document handling and redaction.

- Domain fluency across LOS/CLOS, onboarding, trade finance flows, collections strategies, and audit/compliance expectations.

- Excellent stakeholder leadership and communication; able to translate model and platform trade-offs for executives, customers, and regulators.