AI Platform Architect

BICP, a San Diego-based consulting firm specializing in AI, Data & Analytics, is seeking an experienced AI Platform Architect to join our team in a Forward Deployed capacity with one of our strategic clients in the apparel and retail industry. This is a hands-on architecture role focused on designing and enabling the enterprise platforms, patterns, and technical foundations required to move AI, Machine Learning, GenAI, and Agentic AI solutions from experimentation into secure, scalable production environments. You will work closely with AI Engineering, Data Science, Data Engineering, Product, Security, Enterprise Architecture, and business teams to establish an AI platform that enables teams to innovate quickly while operating within appropriate enterprise guardrails. The ideal candidate combines deep cloud and AI architecture expertise with a pragmatic, builder-oriented mindset. You should be equally comfortable defining architecture and standards, evaluating emerging technologies, solving implementation challenges, and working alongside engineering teams to turn architectural decisions into working solutions. What You'll Do Architect and evolve the enterprise AI/ML and GenAI platform ecosystem, supporting Data Science, ML Engineering, AI Engineering, and Agentic AI workloads. Design scalable architecture patterns for developing, deploying, integrating, monitoring, and governing production AI solutions. Establish technical patterns and reference architectures for LLMs, RAG, AI agents, model serving, inference, orchestration, evaluation, and observability. Design the platform capabilities required to support Agentic AI, including model access, tool integration, APIs, orchestration, memory/context, identity and access controls, observability, and human-in-the-loop patterns. Evaluate emerging AI technologies, frameworks, platforms, and foundation models and determine where they fit within the broader enterprise architecture. Help define build-vs-buy decisions across AI platforms, tooling, model providers, orchestration frameworks, vector databases, evaluation tools, and supporting infrastructure. Partner with Data Science and AI Engineering teams to move models, agents, and AI applications from experimentation into reliable production environments. Establish reusable platform components and engineering patterns that allow teams to build new AI capabilities without recreating foundational infrastructure for every use case. Design secure integration patterns connecting AI applications with enterprise data, APIs, SaaS platforms, operational systems, and business workflows. Partner with Data Engineering and platform teams to ensure AI workloads have scalable, governed access to trusted enterprise data. Work closely with Security, Privacy, Governance, and Enterprise Architecture teams to establish appropriate guardrails for enterprise AI. Define approaches for model and agent evaluation, monitoring, observability, performance, cost management, and lifecycle management. Help establish governance patterns around model access, sensitive data, prompt and response handling, auditability, explainability, and responsible AI. Identify architectural bottlenecks that slow AI experimentation or productionization and develop pragmatic solutions that increase delivery velocity. Translate complex technical architecture into clear recommendations for both engineering teams and senior business and technology stakeholders. Remain hands-on enough to validate architectural decisions through prototypes, reference implementations, technical evaluations, and collaboration with engineering teams. What We're Looking For 10+ years of progressive experience across Cloud Architecture, Data Architecture, Platform Engineering, Machine Learning Engineering, AI Engineering, or related disciplines. Significant experience operating at a Principal, Architect, Staff, Senior Staff, or equivalent technical leadership level. Demonstrated experience architecting modern AI/ML platforms and production AI systems in enterprise environments. Strong understanding of the end-to-end AI lifecycle, including experimentation, development, deployment, inference, monitoring, governance, and continuous improvement. Hands-on experience with modern GenAI architectures, including LLMs, RAG, embeddings, vector search, model APIs, prompt/orchestration frameworks, and evaluation. Strong understanding of Agentic AI architecture, including agent orchestration, tool use/function calling, context management, memory, multi-agent patterns, identity, permissions, and observability. Deep experience with at least one major cloud ecosystem such as Azure, AWS, or GCP and the associated AI/ML services. Experience with modern enterprise data and AI platforms such as Snowflake, Databricks, Azure AI, AWS SageMaker/Bedrock, or equivalent technologies. Strong understanding of APIs, microservices, event-driven architectures, containers, Kubernetes/serverless infrastructure, and modern application integration patterns. Experience designing MLOps/LLMOps capabilities including CI/CD, model versioning, deployment automation, model registries, evaluation, monitoring, and lifecycle management. Strong understanding of enterprise security, IAM, data privacy, governance, networking, and architectural controls as they apply to AI workloads. Experience evaluating emerging technologies and making pragmatic build-vs-buy recommendations based on business requirements, technical fit, scalability, cost, and risk. Ability to operate across both architecture and implementation rather than limiting involvement to high-level design. Strong communication skills with the ability to influence engineering teams, architects, product leaders, and senior stakeholders. Preferred Experience Experience building or modernizing an enterprise AI platform or AI enablement layer used by multiple Data Science and AI Engineering teams. Direct experience taking GenAI or Agentic AI applications into production within a large enterprise. Experience with AI agent frameworks and orchestration technologies such as LangGraph, LangChain, Semantic Kernel, AutoGen, or similar frameworks. Experience with enterprise LLM platforms and model providers such as Azure OpenAI, OpenAI, Anthropic, Amazon Bedrock, or equivalent technologies. Experience designing RAG architectures using vector databases, enterprise search, knowledge graphs, or hybrid retrieval approaches. Familiarity with AI observability, evaluation, guardrails, model routing, and cost/token optimization. Experience working within retail, apparel, DTC, consumer products, or another data-intensive consumer environment. Understanding of Data Science and ML use cases involving forecasting, optimization, personalization, consumer intelligence, merchandising, and planning. The Kind of Person Who Will Thrive in This Role You are an architect who still likes to build. You can think strategically about where an enterprise AI platform needs to go while remaining close enough to the technology to prototype an approach, challenge an engineering decision, troubleshoot an integration, or evaluate a new framework yourself. You understand that enterprise AI requires a balance between speed and discipline. Your instinct isn't to bypass architecture, security, or governance to move faster; it's to create reusable platforms and guardrails that allow teams to move faster within them. You are comfortable operating in ambiguity, working directly with Data Scientists, AI Engineers, Product teams, and business stakeholders, and taking ownership of technical problems that don't arrive with perfectly defined requirements. Most importantly, you view architecture as an enabler of execution rather than a gate to execution. About BICP BICP is a San Diego-based AI, Data & Analytics consultancy that helps organizations close the gap between strategic priorities and the specialized capabilities required to execute them. Our Forward Deployed practitioners work alongside client teams, bringing deep expertise across AI/ML, Data, Analytics, Product, and Architecture to move initiatives from idea to implementation, production, and measurable business impact. Show more Show less