Ai Architect

BrainRidge Consulting is a premium FinTech Technology Consulting firm with the energy of a startup and the experience of an enterprise. With a mission to empower financial institutions and organizations to thrive in the digital age, we provide cutting-edge technology solutions and strategic guidance to drive innovation and growth. As we continue to grow, we are seeking motivated and experienced AI Architects to join our team.
Role OverviewAs an AI Architect, you will design and build enterprise-grade AI solutions, platforms, and agentic systems for our clients. This role sits at the intersection of AI, software architecture, cloud, data, security, and enterprise technology, and you'll work with business and engineering teams to turn complex AI use cases into scalable, secure, production-ready solutions. This role is ideal for someone who goes beyond demos and prompt engineering, with a strong understanding of how to architect AI systems that operate reliably inside large enterprises.
Key Responsibilities
Design end-to-end architectures for Generative AI, Agentic AI, RAG, machine learning, and intelligent automation solutions.
Translate business requirements into scalable technical architectures and implementation approaches.
Architect AI agents, including their tools, workflows, memory, permissions, context, and human-in-the-loop controls.
Design enterprise AI platforms and reusable capabilities, including model gateways and catalogs, agent runtimes and registries, RAG and enterprise knowledge services, prompt and tool registries, and evaluation and observability platforms.
Define guardrails, governance, usage monitoring, and cost controls for enterprise AI systems.
Evaluate and recommend AI models based on capability, latency, security, cost, and use-case requirements.
Design retrieval and knowledge architectures across structured and unstructured enterprise data.
Integrate AI solutions with enterprise applications, APIs, databases, workflows, and systems of record.
Define AI security patterns covering identity, authorization, data protection, prompt injection, tool execution, and agent permissions.
Establish architecture patterns for AI evaluation, observability, testing, monitoring, and production operations.
Define deployment strategies across cloud, hybrid, containerized, serverless, and Kubernetes environments.
Develop reusable AI reference architectures, standards, patterns, accelerators, and architecture decision frameworks.
Lead architecture workshops, technical discovery sessions, and proofs of concept.
Provide technical leadership and guidance to AI engineers, software engineers, data engineers, and platform teams.
Partner with security, data, cloud, enterprise architecture, risk, privacy, and business stakeholders.
Qualifications
7+ years of experience in software or solutions architecture, including meaningful experience architecting AI or machine learning systems.
Strong experience across generative and agentic AI, including LLMs, RAG, multi-agent systems, tool/function calling, context engineering, AI memory, and model routing.
Experience with AI frameworks such as LangChain/LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or MCP, and model ecosystems such as OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Mistral, or Cohere.
Strong experience with at least one major cloud platform (Azure, AWS, or Google Cloud) and its AI services.
Experience with data and knowledge architecture, including SQL/NoSQL databases, data lakes and warehouses, vector databases, hybrid/semantic search, and knowledge graphs.
Strong understanding of enterprise and software architecture, including distributed systems, microservices, REST APIs, event-driven architecture, Kubernetes, serverless computing, and identity and access management.
Hands-on experience with Python, SQL, APIs, Git, CI/CD, Docker, Kubernetes, and Infrastructure as Code.
Familiarity with MLOps/LLMOps/AgentOps practices and observability tools such as MLflow, LangSmith, Langfuse, Arize Phoenix, or Weights & Biases.
Ability to move comfortably between executive conversations and deep technical discussions.
Demonstrated experience working across multiple business domains and with cross-functional technical and business teams.
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