JD AI Solution Architect (Fintech Web, Mobile Agentic)

Role

The AI Solution Architect turns business goals into secure, scalable AI/ML and agentic solutions: reference architecture , integration , and governance with payments-aware risk discipline.

Experience: 12 15 years in software/solution architecture; robust recent exposure to GenAI, agents, and production integrations .

Responsibilities

Area

Expectations

AI agentic design

End-to-end solutions: LLM + tools (e.g. MCP-style) , RAG/memory where grounding helps, guardrails , durable workflows (state, HITL, idempotency for multi-step flows).

Strategy stack

Prioritize high-value use cases; define cloud/hybrid targets ( AWS and/or Azure as applicable), model-agnostic patterns, cost/latency trade-offs.

Integration

APIs and events for web, mobile, and agentic clients; goal-oriented machine-facing contracts alongside traditional channels; integration with CRM (e.g. Salesforce) and core platforms.

Security access

AuthN/AuthZ for users and automated actors (agents, services): least privilege, audit, secrets, no LLM database direct access tools/APIs only .

Operations

Observability for quality, latency, cost,



abuse ; evaluation and improvement loops (not one-off launches).

Compliance ethics

Secure development, data minimization, alignment with fintech/regulatory expectations for AI use.

Evolution

Monolith modular/API-first roadmaps ( strangler , bounded contexts) including agent-ready surfaces.

Technical profile

- AI/ML agents: Practical depth with examples such as PyTorch / TensorFlow (classical ML), LangChain / LangGraph / LlamaIndex or equivalent (orchestration RAG), MLflow or cloud ML ops where used.
- Cloud: AWS (primary ask) and/or Azure ; identity, networking, observability.
- Data: Yes required: pipelines (batch/stream), quality, and timely data for models/agents not optional at architect level.

Integration: REST/events, API gateways; Salesforce and enterprise systems in target-state designs alongside agents .

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