AI Full Stack Architect
CirrusLabs, LLC
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Benefits offered by this job
Job summary
CirrusLabs, LLC seeks an AI Full Stack Architect to lead the end-to-end design of agentic AI systems and full-stack applications at enterprise scale. You will own architecture for LLM-powered platforms using AWS Bedrock, Python, Node.js, and React/Next.js, guiding teams across AI, backend, and frontend layers.
You will mentor engineers, drive RFCs, and shape guardrails for responsible AI while delivering multi‑agent, tool-using capabilities with robust performance and cost controls.
Qualifications
- Experience building autonomous agents and tool-calling agents.
- Hands-on with LangChain, LangGraph, AutoGen, CrewAI or Bedrock Agents.
- Front-end experience with React/Next.js for AI-powered UIs.
- Proficiency in prompt engineering, RAG, few-shot learning, and fine-tuning.
Responsibilities
- Architect autonomous, multi-step, and tool-using AI agents using Bedrock Agents.
- Design multi-agent orchestration topologies and handoff protocols.
- Establish agent design patterns: memory, context, tool sequencing, retries, and recovery.
- Define guardrails, safety layers, and responsible AI standards for all systems.
Skills
Tools
Job description
We are seeking an AI Full Stack Architect to join our AI Center of Excellence. This is a senior individual-contributor and technical leadership role for someone who has spent years in the trenches — building, shipping, and scaling AI-powered systems — and is now ready to set the architectural direction for the next generation of intelligent applications.
You will own the end-to-end design and delivery of agentic AI systems, LLM-powered platforms, and full stack applications that operate at enterprise scale. You bring deep hands‑on expertise across AWS Bedrock, Python, Node.js, and modern front-end frameworks, and you know how to translate that into production‑grade architecture that others can build on.
Architect / Principal — senior IC with org-wide technical influence
10+ years in software engineering; 4+ years in AI/ML engineering
Expert-level in at least two of: agent development, LLM integration, cloud-native backend
Fluent across the full stack — infra, backend, AI layer, and front-end
Comfortable defining the problem, not just solving it
Platform-level — your decisions affect multiple teams and products
- Architect and build autonomous, multi-step, and tool-using AI agents using AWS Bedrock Agents and leading agentic frameworks.
- Design multi-agent orchestration topologies — including supervisor/worker patterns, parallel execution, and handoff protocols.
- Establish agent design patterns: memory management, context windows, tool-call sequencing, retry logic, and failure recovery.
- Define guardrails, safety layers, and responsible AI standards for all agent-based systems.
- Own the selection, integration, and lifecycle management of Large Language Models (Claude, GPT-4, Llama, Mistral) and Small Language Models (Phi-3, Gemma, Mistral 7B).
- Design and implement RAG pipelines, prompt engineering standards, few-shot frameworks, and fine‑tuning workflows.
- Establish model benchmarking and evaluation criteria — balancing capability, latency, cost, and safety.
- Lead the design of AI infrastructure on AWS Bedrock — including foundation model access, Knowledge Bases, and Bedrock Agents.
- Architect cloud-native backend services using Lambda, API Gateway, ECS/EKS, S3, and IAM.
- Define infrastructure‑as‑code standards and CI/CD pipelines for AI workloads.
- Build and architect full stack applications — React/Next.js front ends, Node.js backend services, and Python agent/ML pipelines.
- Design streaming agent UIs, chat interfaces, and real‑time AI‑powered user experiences.
- Own API design and integration patterns between front‑end, backend, and AI layers.
- Implement agent monitoring and evaluation pipelines using Fiddler AI and comparable platforms (LangSmith, Arize, Weights & Biases, Helicone).
- Define and track agent performance metrics: accuracy, latency, hallucination rate, tool‑call success rate, and cost‑per‑interaction.
- Build feedback loops that drive continuous model and agent improvement.
- Lead architecture reviews, design documents, and RFC processes for AI initiatives.
- Mentor and upskill engineers on agent development, LLM integration, and AI best practices.
- Partner with product, data, and platform teams to translate business requirements into scalable AI solutions.
- Continuously evaluate emerging models, frameworks, and tooling — bringing the best to the team.
- AWS Bedrock — deep, hands‑on experience building and deploying agents and models (Bedrock Agents, Knowledge Bases, foundation model APIs).
- Strong working knowledge of the broader AWS ecosystem: Lambda, S3, IAM, API Gateway, ECS/EKS, CloudWatch.
- Experience designing cloud‑native, serverless, and containerized AI workloads.
- Proven, hands‑on experience building autonomous agents, tool‑calling agents, ReAct / plan‑and‑execute patterns, and multi‑agent systems.
- Deep experience with agentic frameworks: LangChain, LangGraph, AutoGen, CrewAI, or AWS Bedrock Agents.
- Strong command of agent memory, context management, tool use, and orchestration design.
- Front‑end — proficient in React or Next.js; able to build and architect AI‑powered UIs including streaming chat and agent interfaces.
- Hands‑on production experience with LLMs (Claude, GPT‑4, Llama, Mistral) and SLMs (Phi‑3, Gemma, Mistral 7B).
- Deep practical knowledge of prompt engineering, RAG, few‑shot learning, and fine‑tuning workflows.
- Experience owning model evaluation, selection, and cost/capability trade‑off decisions.
- Hands‑on experience with Fiddler AI or comparable monitoring platforms (LangSmith, Arize, W&B, Helicone).
- Ability to define, instrument, and track agent and model performance metrics end‑to‑end.
- Experience with vector databases (Pinecone, pgvector, OpenSearch, Weaviate) for semantic search and RAG pipelines.
- Familiarity with MLOps practices — CI/CD for models, model versioning, A/B evaluation, drift detection.
- Exposure to multi‑modal models (vision + language).
- Prior experience in an AI product or platform team at scale, in a full stack or architect capacity.
- AWS certifications — AWS Certified Solutions Architect (Professional) or AWS Certified Machine Learning — Specialty.
You write code, ship agents, and debug production issues — not just architect on whiteboards
You see the system, not just the feature — and design for scale, reliability, and maintainability
You make the engineers around you better through mentorship, documentation, and clear technical direction
You stay current with the fast-moving AI landscape and bring new ideas to the team
You take a feature from idea to production and care about it long after launch
You can explain complex AI behavior to non-technical stakeholders without dumbing it down
- Work on cutting‑edge agentic AI systems deployed at enterprise scale.
- Be part of the AI Center of Excellence — a team dedicated to pushing the boundaries of what AI can do inside and outside the organization.
- Collaborative, low-ego culture with a strong bias for shipping.
- Competitive compensation, flexible work arrangements, and continuous learning support.
- Direct influence over the AI platform architecture that the entire organization builds on.
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