Ai Full Stack Architect

Key Responsibilities

Agent Architecture & Development

• 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.

LLM / SLM Integration

• 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.

Cloud & Infrastructure (AWS)

• 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.

Full Stack Development

• 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.

Monitoring, Observability & Evaluation

• 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.

Technical Leadership

• 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.

Required Qualifications

AWS & Cloud

• 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.