1 month ago
Noida, IndiaSenior
Responsibilities
- Assess end-to-end payment operations and identify automation opportunities, bottlenecks, control weaknesses, integration failures, data gaps, and architectural complexity.
- Define prioritized Payments AI and automation roadmaps based on value, risk, feasibility, scalability, and ROI.
- Design the AI-native Payments Operating System, including agent architecture, orchestration, data pipelines, model access, integrations, controls, reporting, and observability.
- Define patterns for AI gateways, model selection, prompt and agent registries, RAG and knowledge services, workflow engines, human approval, and auditability.
- Evaluate and simplify the payments technology landscape and support gradual migration from the current stack to a future-state platform.
- Incorporate security, data governance, model risk, explainability, resilience, privacy, and regulatory controls into architecture.
- Lead architecture reviews, proof-of-value initiatives, technical standards, reusable frameworks, and platform capability development.
- Represent Payments within the AI Center of Excellence and align enterprise AI, data, cloud, security, and governance strategies.
- Communicate architecture, investment decisions, risks, and roadmaps to technical, business, risk, compliance, audit, and executive stakeholders.
- Mentor engineers and provide technical leadership from discovery through production optimization.
Requirements
- 10+ years of experience in software engineering, solution architecture, enterprise architecture, or platform engineering.
- At least 3 years of experience designing generative AI, LLM, machine-learning, or intelligent automation solutions.
- Experience leading architecture for enterprise-scale transformation programs.
- Strong Payments or Financial Services experience, ideally involving payment processing, reconciliation, settlement, ledgers, routing, or operational controls.
- Experience defining target-state architecture, transition architecture, technology roadmaps, and implementation governance.
- Experience engaging senior business, engineering, risk, compliance, audit, and executive stakeholders.
- Knowledge of LLM platforms, multi-agent systems, tool calling, RAG, embeddings, vector stores, model gateways, evaluation, and guardrails.
- Experience with agent orchestration frameworks such as Spring AI, LangGraph, LangChain, MCP, Semantic Kernel, AutoGen, or comparable frameworks.
- Knowledge of microservices, event-driven architecture, API strategy, integration platforms, domain-driven design, and distributed systems.
- Knowledge of Kubernetes, cloud platforms, Kafka or event streaming, API gateways, CI/CD, observability, security, IAM, and data pipelines.
- Knowledge of governance practices including risk tiering, data classification, model risk, access control, traceability, explainability, human oversight, and audit logging.
- Experience with value assessment, architectural trade-offs, dependency management, phased migration, cost modeling, and ROI measurement.
- Preferred experience with ISO 20022, SWIFT, ACH, real-time payments, card networks, merchant acquiring, payment gateways, or treasury operations.
- Preferred knowledge of PCI DSS, SOC 2, model risk management, responsible AI, privacy regulations, and banking control frameworks.
- Preferred TOGAF, cloud architecture, security, or payments-related certifications.
- Experience establishing AI Centers of Excellence, engineering standards, reference architectures, or reusable platform services is preferred.
- Experience balancing legacy modernization with incremental delivery and measurable business outcomes is preferred.
Tech Stack
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