Solution Architect

ROLE OVERVIEW

We are seeking a seasoned Solution Architect to lead the design and delivery of enterprise-grade AI Agent and integration solutions. This role sits at the intersection of AI strategy and enterprise systems engineering — translating complex business requirements into scalable, governed, and observable agentic architectures. You will work closely with product, data, and engineering teams to define blueprints that power next-generation AI capabilities across our fintech platform.

KEY RESPONSIBILITIES

• •    Architect end-to-end AI Agent solutions leveraging LLM and Agentic frameworks (LangChain, AutoGen, CrewAI) integrated with enterprise systems.
• •    Design API-first and event-driven integration architectures ensuring reliability, scalability, and low-latency across services.
• •    Define governance frameworks for AI agents including access control, audit trails, policy enforcement, and responsible AI guardrails.
• •    Establish observability standards using LaunchDarkly, feature flagging strategies, and DevOps pipelines for AI-driven systems.
• •    Collaborate with stakeholders across Engineering, Data, Product, and Risk to align architecture decisions with business outcomes.
• •    Create and maintain Technical Solution Architecture documentation, including architecture decision records (ADRs), data flows, and system diagrams.
• •    Evaluate and recommend emerging AI tools, LLM providers, and orchestration platforms for enterprise adoption.
• •    Provide technical leadership and mentoring to engineering squads implementing agentic solutions.

REQUIRED SKILLS & EXPERIENCE

• •    8+ years of Solution Architecture or Technical Architecture experience in enterprise environments.
• •    Deep expertise in AI Agent Architecture and Large Language Model (LLM) ecosystems.
• •    Hands-on experience with Agentic Frameworks — LangChain, AutoGen, CrewAI, or equivalent.
• •    Strong background in Enterprise Integration Architecture: RESTful APIs, event-driven systems (Kafka, Event Grid), microservices.
• •    Proficiency with observability tooling and feature flagging platforms, including LaunchDarkly.
• •    Demonstrated ability to design AI governance and policy frameworks in regulated industries.
• •    Experience in financial services, fintech, or banking environments preferred.
• •    Excellent communication skills with the ability to present complex architectures to executive and technical audiences.

PREFERRED QUALIFICATIONS

• •    Cloud architecture certifications (AWS, Azure, GCP) with AI/ML specialization.
• •    Experience with MLOps tooling, model registries, and LLMOps pipelines.
• •    Familiarity with compliance frameworks: SOC2, FFIEC, model risk management (SR 11-7).
• •    Prior experience contributing to enterprise AI Centers of Excellence (CoE).