Solution Architect- AI Agent

Incedo Inc
New York, NY, United States
21 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Job source

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Applications Architecture Application Performance Management Architectural Patterns Audit Trail Business Software Cloud Engineering Data as a Services Data Governance Software Design Patterns DevOps
+23 more
Identity and Access Management Data Logging Enterprise Software Applications LangChain Large Language Models Multi-Agent Systems Prompt Engineering Reliability of Systems Agentic-AI Event Driven Architecture Containerization Kubernetes CrewAI Performance Monitor AutoGen Enterprise Integration Virtual Agents Evaluation of Large Language Models Api Gateway Restful APIs Dynatrace Legacy Systems Microservices

Job description

Incedo Inc. is seeking a 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. The ideal candidate will combine technical expertise with strong architectural leadership to deliver secure, scalable, and observable technology solutions., * 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 scalable integration architectures connecting AI agents with enterprise platforms, APIs, data services, and business applications.
  • Translate business requirements and solution concepts into detailed architecture designs, technical specifications, and implementation roadmaps.
  • Define observability strategies covering AI agent behavior, application performance, API transactions, workflow execution, and system reliability.
  • Collaborate with business stakeholders, product teams, enterprise architects, and engineering leaders to align solution designs with business objectives.

Requirements

  • 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., * 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.

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