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We operate at the intersection of data foundations and applied AI, helping clients move from research concepts to real, deployed systems.We are a small, sharp consulting team. We ship real systems, not just decks. As a Principal AI Architect, you are the senior technical authority responsible for ensuring our AI solutions are not just impressive demos, but durable, governable, and operationally sound. This is a hands-on principal IC role, not a people-management position.You are the technical decision-maker for AI delivery at Fastloop. You bridge the gap between the art of the possible and production reality, owning decisions across model selection, RAG strategy, agent orchestration, security posture, and deployment architecture.You lead by doing. You write the core scaffolding code, define the architectural patterns others extend, and act as the final technical gatekeeper for production AI releases. When projects hit complexity cliffs, you are called in to unblock them.While you do not formally manage people, you are expected to teach, mentor, and elevate the technical bar across the team. What You’ll Do1. Agentic Intelligence & Reasoning <ul><li>Architect agentic workflows: Design multi-step, stateful agent systems using LangGraph, CrewAI, Semantic Kernel, or equivalent frameworks.</li><li>Beyond prompting: Define how agents reason, plan, and act. Define tool selection, memory, guardrails, and failure handling.</li><li>Application Logic & Cognitive Architecture: Build the complex chains, prompt orchestration, reasoning loops, and multi-agent workflows. Maintain conversation state, manage token limits, and handle tool-calling errors gracefully in production.</li><li>Advanced RAG: Design retrieval systems end-to-end: chunking strategy, embeddings, indexing, hybrid search, grounding, and citation.</li><li>Evaluation & Metrics Ownership: Design robust evaluation frameworks (LLM-as-a-Judge, Ragas, TruLens, Arize) to measure model performance, latency, and accuracy before production deployment. Own the truth of the system and prove that AI solutions work reliably.</li><li>Custom tooling: Build APIs and tools when off-the-shelf connectors fall short. </li></ul> 2. Technical Leadership & Delivery<ul><li>Hands-on ownership: Personally implement high-impact components, core services, agent frameworks, complex integrations, and automation logic.</li><li>Rapid Prototyping for Presales: Quickly build working demos or POC’s to validate feasibility and secure client engagements.</li><li>Collaboration with Engineers:</li><li>Work closely with Engineers on pipelines and infrastructure while taking ownership of cloud, agent orchestration, and deployment architecture.</li><li>Infrastructure & deployment: Define and contribute to IaC (Terraform), CI/CD pipelines, environment strategy, and release processes.</li><li>Cloud focus: Lead both GCP and Microsoft Azure technical work, filling gaps where our team has limited experience.</li><li>Security & governance: Own the production-readiness checklist for AI systems, including PII handling, IAM, VPC boundaries, secrets management, and auditability.</li><li>Engineering validation: Build rapid technical spikes and POCs to move initiatives from assumptions to proof and de-risk delivery.</li></ul>You hold final technical sign-off on AI deployments. 3. Strategic Consulting & Practice Enablement<ul><li>Pre-sales feasibility: Partner with the Director of Engineering and leadership to assess opportunities, prevent over-selling, and define realistic delivery paths.</li><li>Reusable accelerators: Convert successful project patterns into reusable modules, templates, and reference architectures that accelerate future delivery.</li><li>Client advisory: Translate technical trade-offs (latency vs. accuracy, cost vs. performance, managed vs. custom) into business-relevant decisions for senior stakeholders.</li><li>Dedicate approximately 20% of your time to mentoring team members, performing code reviews, and shaping Fastloop’s AI standards, frameworks, and internal best practices.</li></ul> Your Technical ToolkitAI & LLMs<ul><li>Production experience with Vertex AI, OpenAI, Azure AI</li><li>Strong evaluation discipline using LangSmith, Arize Phoenix, or equivalent</li><li>Expert in Cognitive Architectures for multi-agent systems, using advanced patterns such as planning, reflection, and tool-use to solve complex multi-step business problems.</li><li>Deep experience with agent state management (LangGraph checkpoints, conversation history, error handling).</li><li>Production AI beyond notebooks, including RAG, A2A, MCP orchestration, and agentic workflows.</li><li>Model evaluation, latency, cost, and performance optimization.</li><li>MLOps pipelines, including automated training, CI/CD, monitoring, and deployment.</li></ul>Engineering<ul><li>Expert-level Python (async, FastAPI, custom tooling, service design)</li><li>Strong SQL and data modeling fundamentals</li><li>Collaborate with Data Engineers to ensure AI systems integrate seamlessly with client data pipelines, warehouses, and orchestration workflows.</li></ul>Infrastructure & Ops<ul><li>Hands-on experience with GCP and Microsoft Azure</li><li>CI/CD with GitHub or GitLab</li><li>Terraform and infrastructure-as-code best practices</li></ul>Data & Systems<ul><li>Vector databases and search (BigQuery Vector Search, Pinecone, or similar)</li><li>Solid understanding of enterprise networking, identity, and workload isolation</li><li>Solid understanding of data platforms like Synapse, Databricks and Snowflake</li></ul>Frameworks & Platforms<ul><li>LangChain, LangGraph, Googles ADK</li><li>dbt / Dataform</li></ul> Experience & Background<ul><li>10+ years in software or data engineering with a clear evolution toward applied AI</li><li>3+ years building, deploying, and operating LLM-based systems in production</li><li>Strong consulting instincts: able to provide technical clarity under ambiguity and stakeholder pressure</li><li>Comfortable owning outcomes end-to-end, not just architecture diagrams</li></ul> Were looking for demonstrated experience that highlights: <ul><li>Your Consulting DNA: You thrive in multi-client environments and can provide technical clarity under high-pressure stakeholder scenarios.</li><li>The “Grit” Factor: You prefer a “sharp” team over a “big” one. You value ownership, clarity, and building the hardest parts first.</li><li> </li></ul> Ready to build real AI systems? We want to hear from you.

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Principal AI Architect

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Fastloop

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