Mid-Level Enterprise / Unified Architecture (UA) Architect

Mid-Level Enterprise / Unified Architecture (UA) Architect - AI & Financial Services

NYC midtown - 3 days a week onsite

Right to hire – 6 months to convert (no visa sponsorship available at this time)

Onsite Interview required

Position Summary

We are seeking a highly motivated Mid-Level UA Architect with a strong background in Artificial Intelligence (AI), Enterprise Architecture, and Financial Services to help drive the design and implementation of next-generation technology solutions. This role will bridge business strategy, enterprise architecture, AI innovation, and solution delivery to enable scalable, secure, and compliant digital capabilities across the organization.

The ideal candidate will have experience defining target-state architectures, integrating AI/ML capabilities into enterprise platforms, and working within highly regulated financial environments. This individual will collaborate with business executives, product teams, engineering leaders, risk, compliance, and data teams to deliver architecture solutions that support strategic transformation initiatives.

Key Responsibilities

Enterprise & Solution Architecture

• Design and maintain enterprise architecture blueprints aligned with business objectives and technology strategy.
• Define current-state, future-state, and transition architectures for enterprise-wide initiatives.
• Create architecture artifacts, standards, governance frameworks, and technology roadmaps.
• Evaluate and recommend emerging technologies, platforms, and vendor solutions.

AI & Data Architecture

• Lead architecture activities for AI and Generative AI initiatives across business domains.
• Design scalable AI-enabled solutions incorporating machine learning, large language models (LLMs), intelligent automation, and advanced analytics.
• Establish AI architecture patterns addressing model integration, governance, explainability, observability, and lifecycle management.
• Collaborate with data engineering teams to define data architecture supporting AI and analytics workloads.
• Ensure responsible AI principles, model governance, and regulatory compliance are integrated into solution designs.

Governance & Stakeholder Management

• Participate in architecture review boards and governance processes.
• Engage with executives, business stakeholders, product owners, and technology teams to ensure alignment.
• Translate business requirements into scalable architecture solutions.
• Provide technical leadership and mentorship to development and engineering teams.

Cloud & Modernization

• Define cloud-native architecture patterns leveraging Azure, AWS, or Google Cloud.
• Support modernization initiatives involving microservices, APIs, event-driven architectures, and container platforms.
• Develop reference architectures for enterprise AI and digital transformation programs.

Experience

• 5-8 years of experience in enterprise architecture, solution architecture, or technology consulting.
• Hands-on experience implementing AI/ML or Generative AI solutions in enterprise environments.
• Experience working with large-scale digital transformation programs.

Technical Skills

• Enterprise Architecture frameworks (TOGAF, Zachman, FEAF, or equivalent).
• AI/ML platforms and tools.
• Generative AI, LLMs, RAG architectures, vector databases, and prompt engineering.
• API design and integration patterns.
• Cloud platforms (Azure, AWS, GCP).
• Data architecture, data governance, and metadata management.
• Microservices, event-driven architecture, and distributed systems.
• Security architecture and identity management.
• DevSecOps, CI/CD pipelines, and Infrastructure as Code.

Preferred Qualifications

• TOGAF or equivalent architecture certification.
• Cloud certifications (Azure Architect, AWS Solutions Architect, GCP Architect).
• AI certifications from Microsoft, AWS, Google, OpenAI, or similar providers.
• Experience with:
• Microsoft Azure AI Services
• Azure OpenAI
• Databricks
• Snowflake
• AWS Bedrock
• Vertex AI
• MLOps platforms
• Experience designing AI governance and responsible AI frameworks.
• Knowledge of model risk management within financial institutions.

Competencies

• Strategic thinking and business alignment.
• Strong communication and stakeholder management skills.
• Executive-level presentation capabilities.
• Ability to balance innovation with risk management and regulatory requirements.
• Strong problem-solving and analytical thinking.
• Collaborative leadership and influence without authority