Solutions Architect

Job Description

AI Full Stack Architect

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Status: Must be legally eligible to work where you live.

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Resume must be in English

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Location : Remote, some travel to Atlanta, GA

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Engagement : Contract, Long Term, Full Time

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Job Description — AI Center of Excellence

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About the Role

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We are seeking an AI Full Stack Architect to join our AI Center of Excellence (AI COE) — a senior, hands-on technical leader who can both design enterprise-grade AI systems and roll up their sleeves to build them. This is not a whiteboard-only role. You will own the architecture of our agentic AI platform end-to-end — from cloud infrastructure and backend services to LLM pipelines, agent orchestration, and front-end experiences — while setting the technical direction for the team around you.

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This role is ideal for someone who has built and shipped production AI agents, has deep AWS Bedrock expertise, and can lead architecture decisions while staying close to the code.

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Level & Scope

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• Seniority: Architect / Principal Engineer
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• Experience: 10+ years in software engineering; 4+ years in AI/ML engineering
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• Experience building autonomous bots - not just agents
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• Scope: Platform-wide — agents, LLM/SLM integration, observability, full stack
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• Leadership: Technical lead; mentors engineers, drives standards and reviews
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• Ambiguity: Defines the solution when none exists; navigates fast-moving AI landscape
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• Impact: Decisions affect platform reliability, cost, and AI product quality at scale
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Key Responsibilities

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AI Agent Architecture & Development

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• Architect and lead the design of autonomous, multi-step, and tool-using AI agent systems — including agent topology, orchestration patterns, memory strategies, and failure recovery.
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• Define and enforce standards for ReAct, plan-and-execute, and multi-agent collaboration patterns across the platform.
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• Own hands-on development of complex agent pipelines using AWS Bedrock Agents, LangChain, LangGraph, AutoGen, CrewAI, or equivalent frameworks.
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• Design agent guardrails, safety layers, and fallback mechanisms for production reliability.
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LLM & SLM Integration

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• Lead the evaluation, selection, and integration of Large Language Models (e.g., Claude, GPT-4, Llama, Mistral) and Small Language Models (e.g., Phi-3, Gemma, Mistral 7B) into production workflows.
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• Own prompt engineering standards, few-shot learning strategies, RAG (Retrieval-Augmented Generation) pipeline architecture, and fine-tuning workflows.
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• Drive model benchmarking and cost/capability trade-off decisions at the platform level.
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Cloud & Infrastructure (AWS)

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• Architect and own AI workloads on AWS Bedrock — including foundation model access, Knowledge Bases, and Bedrock Agents.
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• Design scalable, secure, and cost-efficient cloud infrastructure leveraging Lambda, API Gateway, S3, IAM, ECS/EKS, and related AWS services.
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• Define infrastructure-as-code standards and CI/CD pipelines for AI model and agent deployments.
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Full Stack Development

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• Lead architecture and hands-on development of full stack AI-powered applications — responsive front-end UIs, backend APIs, and agent orchestration layers.
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• Set standards for Node.js backend services, Python agent and ML pipelines, and modern front-end frameworks (React, Next.js, or Vue).
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• Architect streaming agent response patterns, tool-call UIs, and real-time agent interaction experiences.
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Agent Monitoring, Observability & Evaluation

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• Own the agent observability strategy — define what gets measured, how, and why.
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• Implement and maintain monitoring pipelines using Fiddler AI and comparable platforms (LangSmith, Arize, Weights & Biases, Helicone).
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• Define and track key agent performance metrics: accuracy, latency, hallucination rate, tool-call success rate, and cost per inference.
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• Establish evaluation frameworks for continuous model and agent quality assurance.
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Technical Leadership & Governance

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• Lead architecture reviews, define technical standards, and maintain platform documentation.
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• Mentor and upskill engineers across the AI COE on agent development, LLM best practices, and responsible AI.
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• Collaborate with product, engineering, and business stakeholders to translate complex requirements into actionable, scalable AI solutions.
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• Contribute to the organization's responsible AI principles — bias detection, explainability, and model governance.
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Required Qualifications

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Cloud & Infrastructure

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• AWS Bedrock — deep, hands-on experience building and deploying agents and models (foundation model access, Knowledge Bases, Bedrock Agents).
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• Strong command of the broader AWS ecosystem: Lambda, S3, IAM, API Gateway, ECS/EKS, CloudWatch.
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• Experience designing secure, scalable cloud architectures for AI workloads.
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Agent Development

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• Proven, hands-on experience architecting and building AI agents — autonomous agents, tool-calling agents, ReAct / plan-and-execute patterns, and multi-agent orchestration systems.
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• Deep expertise with agentic frameworks: LangChain, LangGraph, AutoGen, CrewAI, or AWS Bedrock Agents.
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• Strong understanding of agent memory, context window management, tool use, and state machines.
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• Experience designing agent guardrails, safety constraints, and production-grade failure handling.
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Languages & Frameworks

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• Python — expert-level; ML pipelines, agent logic, data processing, API development, and scripting.
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• Node.js — expert-level; API services, backend orchestration layers, streaming integrations.
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• Front-end — working proficiency in React, Next.js, or Vue for AI-powered UIs and agent chat experiences.
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LLM / SLM Expertise

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• Hands-on production experience with Large Language Models (Claude, GPT-4, Llama, Mistral) and Small Language Models (Phi-3, Gemma, Mistral 7B).
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• Deep practical knowledge of prompt engineering, few-shot learning, RAG pipelines, and fine-tuning.
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• Experience benchmarking models and making architecture-level model selection decisions.
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Monitoring & Observability

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• Hands-on experience with Fiddler AI or comparable agent/model monitoring platforms (LangSmith, Arize, Weights & Biases, Helicone).
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• Ability to define, implement, and own agent performance metric frameworks at the platform level.
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Preferred Qualifications

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• Experience with vector databases (Pinecone, pgvector, OpenSearch, Weaviate) for semantic search and RAG architectures.
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• Strong MLOps background — CI/CD for models, model versioning, A/B evaluation, drift detection.
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• Exposure to multi-modal models (vision + language) and their integration patterns.
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• Prior experience leading AI platform or product engineering at scale in a full stack capacity.
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• Experience designing streaming response architectures and real-time agent UIs.
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• AWS Certifications: AWS Certified Solutions Architect — Professional and/or AWS Certified Machine Learning — Specialty strongly preferred.
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What We're Looking For

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• Hands-on Architect: You write production code, not just diagrams — you ship agents and own them post-launch.
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• Architectural Thinker: You see the system holistically — trade-offs, failure modes, scalability, and cost at once.
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• Curiosity-Driven: You track the AI landscape weekly and bring new frameworks and ideas to the team proactively.
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• Ownership Mindset: You take a feature from idea to production and care about reliability, cost, and quality long after launch.
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• Multiplier: You raise the technical bar of every engineer around you through reviews, mentorship, and documentation.
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• Clear Communicator: You translate complex AI behavior and architecture decisions for non-technical stakeholders without losing precision.
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Why Join Us

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• Architect and shape cutting-edge agentic AI systems deployed at enterprise scale.
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• Be a founding technical voice in the AI Center of Excellence — a team dedicated to pushing the boundaries of what AI can do inside and outside the organization.
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• Collaborative, low-ego culture with a strong bias for shipping real things.
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• Competitive compensation, flexible work arrangements, and a dedicated continuous learning budget.
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• Direct influence over the AI platform roadmap and technology choices.
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