Principal, AI Architect

The Baldwin Group•Tampa, FL

20h

About The Position

The Principal, AI Architect is the organization’s senior-most technical authority for designing and guiding the development of scalable, secure, and compliant AI platforms and capabilities. This role defines architectural direction, establishes engineering standards and reference patterns, and provides strategic technical leadership across multiple business and technology teams. Operating as an expert-level individual contributor, the Principal, AI Architect translates business use cases into robust platform designs, drives decisions on tooling, frameworks, and model integration, and ensures that AI systems meet enterprise requirements for performance, reliability, and regulatory compliance. This role tackles multi-dimensional, highly complex technical challenges with substantial autonomy. Additionally, this role influences senior leadership, partners across functions including Data, Product, Security, and Enterprise Technology, and provides guidance to engineering teams building AI solutions. The role introduces new architectural approaches, evaluates emerging technologies, and shapes the long-term roadmap for the firm’s AI ecosystem.

Requirements

Deep technical expertise in AI/ML platform architecture, including LLMs, RAG pipelines, vector databases, agent frameworks, orchestration systems, and prompt/response patterns.
Expert-level AWS knowledge (e.g., Bedrock, SageMaker, Lambda, ECS/EKS, API Gateway, IAM, KMS, networking, security controls).
Strong understanding of enterprise integration patterns, API design, event-driven architectures, service mesh, and distributed systems engineering.
Advanced knowledge of secure design principles, compliance frameworks, and architectural controls for regulated environments (SOX, SOC 2, PCI, PII, data residency).
Ability to simplify highly complex concepts, communicate architectural decisions to technical and non-technical stakeholders, and influence senior leadership.
Strong analytical and problem‑solving skills, capable of evaluating multiple technical paths and making sound recommendations under ambiguity.
Demonstrated ability to drive architectural alignment across cross-functional engineering teams.
Proficiency with cloud-native observability, monitoring, scalability design, and performance tuning for AI workloads.
Ability to evaluate emerging AI technologies, model providers, and frameworks, and integrate them responsibly into a long-term architectural roadmap.
High degree of ownership, judgment, and technical leadership with the ability to operate independently and make decisions with wide organizational impact.
Bachelor’s degree in computer science, engineering, or related field required; master’s degree preferred.
8–10+ years of progressive software/platform engineering experience, including substantial depth in AI/ML system architecture.
Hands-on experience designing and deploying production AI systems using LLMs, RAG pipelines, orchestration frameworks (e.g., LangChain, LlamaIndex), and cloud-native AI services.
Significant experience with AWS cloud architecture, including design of scalable, secure, highly available distributed systems.
Demonstrated ability to influence cross-functional leaders, drive architectural standardization, and guide complex technical initiatives.
Strong written and verbal communication skills, including production of decision-quality technical documentation.
Proven ability to lead architecture for large-scale platforms and guide engineering teams in adopting modern, standardized patterns.

Nice To Haves

Experience operating in regulated industries or environments with strong compliance expectations preferred (financial services, insurance, or equivalent).

Responsibilities

Define and maintain the enterprise‑grade AI platform architecture, including orchestration layers, LLM integration patterns, vector/RAG pipelines, agent frameworks, API mesh, and reusable shared services.
Develop architectural standards, guardrails, and reference designs governing how engineering teams build and integrate AI workloads.
Translate business and product requirements into detailed technical designs, ensuring scalability, security, reliability, and alignment with enterprise architecture principles.
Evaluate build‑vs‑buy decisions for AI components, frameworks, and cloud services; develop recommendations and influence cross‑functional stakeholders.
Review solution designs from engineering teams; identify risks, surface architectural gaps, and recommend long‑term, sustainable approaches.
Partner with Security, Compliance, Legal, and Risk to embed SOX, SOC 2, PCI, PII, data residency, and other regulatory requirements into architectural designs.
Collaborate with Data Platform teams to define data access patterns, boundaries, governance expectations, and integration mechanisms aligned with enterprise data strategy.
Lead development of architectural decision records (ADRs), integration specifications, and platform documentation supporting engineering and compliance readiness.
Assess LLM providers, model hosting patterns (e.g., Bedrock, SageMaker, open-source models), and orchestration frameworks; continuously recommend improvements based on evaluation of emerging capabilities.
Drive cross-functional alignment on AI design choices and platform evolution; facilitate architectural reviews, design sessions, and technical deep dives.
Provide expert-level guidance and mentorship to engineering teams implementing AI services, pipelines, and production applications.
Ensure AI solutions follow cloud best practices across reliability, cost efficiency, observability, performance, and operational readiness.
Partner with Product, Engineering, and Enterprise Architecture to influence long-term platform direction, including multi-tenant patterns, internal developer experiences, and future-state AI services.
Support readiness for audits and risk assessments by ensuring AI platform design documentation, logs, and controls meet enterprise and regulatory expectations.
Champion engineering excellence, standardization, and modernization through clear architectural principles, platform governance, and adoption of best‑in‑class tools and patterns.