Solution Architect - AI Agent & Integration (New York)

Company: Incedo Inc.

Website:

Position: Solution Architect – AI Agent & Integration

Location: New York, NY

Type: Full-Time

About Incedo

Incedo Inc. is a high-growth Digital, Data and AI Transformation Specialist firm headquartered in New Jersey. We are a long-term strategy execution partner for Fortune 500 enterprises, operating at the intersection of business and technology. We serve clients across 5 key industries – Banking & Payments, Wealth Management, Telecom, Hi-Tech and Life Sciences.

Incedo delivers ROI from AI @ Scale by leveraging the ‘Power of 3’:

• Deep domain expertise: Deep understanding of industries with an ability to identify and solve specific business problems.
• AI & Data: Integration of Data and AI capabilities with business priorities to deliver impact and ROI.
• Engineering & Operations: Excellence for building digital platforms, driving modernization, integration and tech operations.

With a team of 4,500+ professionals, we have offices across the U.S., Mexico, Canada, and India.

Position Overview

Incedo is seeking an experienced Solution Architect – AI Agent & Integration to design and guide the implementation of enterprise-grade AI agent solutions and integration architectures within the wealth management domain.

This role will focus on architecting intelligent, agent-driven solutions powered by Large Language Models (LLMs), agentic frameworks, and modern enterprise integration patterns. The ideal candidate will combine hands-on technical expertise with strong architectural leadership, translating business requirements into secure, scalable, observable, and governed technology solutions.

The Solution Architect will work closely with business stakeholders, engineering teams, enterprise architects, and technology leadership to define solution blueprints, establish architectural standards, and drive AI-enabled capabilities from design through implementation and production deployment.

Key Responsibilities

AI Agent Architecture & Design

• Design and define enterprise AI agent architectures leveraging LLMs, agentic workflows, and orchestration frameworks.
• Evaluate and implement agentic frameworks such as LangChain, AutoGen, and CrewAI to support enterprise use cases.
• Architect multi-agent systems, tool integrations, workflow orchestration, and context management capabilities aligned with business and technical requirements.
• Establish reusable architectural patterns for integrating AI agents with enterprise applications, data sources, and business processes.
• Define appropriate patterns for human-in-the-loop approvals, exception handling, and controlled execution of agent-driven actions.

Enterprise Integration Architecture

• Design scalable integration architectures connecting AI agents with enterprise platforms, APIs, data services, and business applications.
• Define API-led and event-driven integration patterns to support reliable, secure, and loosely coupled systems.
• Establish standards for API design, service orchestration, messaging, error handling, resiliency, and system interoperability.
• Evaluate integration approaches based on scalability, performance, maintainability, and enterprise architecture standards.

Technical Solution Architecture & Governance

• Translate business requirements and solution concepts into detailed architecture designs, technical specifications, and implementation roadmaps.
• Lead architecture reviews and provide technical direction to engineering and development teams throughout the delivery lifecycle.
• Establish architecture principles, design standards, reusable components, and governance practices for AI and integration solutions.
• Ensure solutions address security, access control, data protection, auditability, reliability, and regulatory considerations relevant to financial services.
• Identify architectural risks, technical dependencies, and implementation challenges, recommending practical mitigation strategies.

Observability, DevOps & Production Readiness

• Define observability strategies covering AI agent behavior, application performance, API transactions, workflow execution, and system reliability.
• Establish monitoring, logging, tracing, alerting, and operational support patterns for production AI and integration platforms.
• Leverage feature management and controlled rollout capabilities, including LaunchDarkly, to support experimentation, feature flags, and progressive deployments.
• Partner with DevOps and platform engineering teams to integrate architectural solutions into CI/CD pipelines and automated deployment processes.
• Define production readiness standards, performance benchmarks, failure recovery mechanisms, and ongoing optimization practices.

Stakeholder Collaboration & Technical Leadership

• Collaborate with business stakeholders, product teams, enterprise architects, and engineering leaders to align solution designs with business objectives.
• Communicate complex technical concepts, architecture decisions, and trade-offs to both technical and nontechnical audiences.
• Provide architectural guidance to development teams, ensuring implementation aligns with approved designs and enterprise standards.
• Stay current with emerging AI agent technologies, LLM capabilities, integration patterns, and enterprise architecture practices.

Required Qualifications

• Extensive experience in solution architecture, enterprise architecture, application architecture, or a related technical leadership role.
• Demonstrated experience designing and delivering enterprise-grade AI, LLM-powered applications, or agentic solutions.
• Strong understanding of LLM architecture, agent orchestration, tool calling, context management, and multi-agent design patterns.
• Hands-on experience with one or more agentic frameworks, such as LangChain, AutoGen, or CrewAI .
• Strong experience designing enterprise integration solutions using REST APIs, API gateways, microservices, and event-driven architectures.
• Experience producing architecture diagrams, technical design documents, integration specifications, and implementation blueprints.
• Knowledge of observability practices, including centralized logging, distributed tracing, metrics, and alerting.
• Familiarity with DevOps practices, CI/CD pipelines, feature flags, and controlled software releases; experience with LaunchDarkly is a plus.
• Understanding of enterprise security, identity and access management, data governance, auditability, and production reliability requirements.
• Strong communication, stakeholder management, and technical decision-making skills.

Preferred Qualifications

• Experience within wealth management, investment management, brokerage, banking, or broader financial services.
• Experience integrating AI-enabled applications with complex enterprise platforms and legacy systems.
• Familiarity with cloud-native architecture, containerization, Kubernetes, and enterprise application deployment patterns.
• Experience with LLM evaluation, guardrails, prompt management, agent performance monitoring, and responsible AI practices.
• Understanding of financial services regulatory, privacy, and operational risk considerations.
• Experience establishing architecture governance frameworks and reusable enterprise technology standards.

What Success Looks Like

• Well-defined, scalable, and secure architectures for AI agent and enterprise integration solutions.
• Successful integration of AI capabilities with existing enterprise systems and business workflows.
• Consistent application of architecture standards, governance controls, and production readiness practices.
• Improved system observability, operational reliability, and controlled deployment of new AI capabilities.
• Effective collaboration across business, architecture, engineering, and platform teams to deliver measurable business value.