Senior Full Stack AI Engineer (AI-Native Systems/Claude Code Expert)

Role Overview:

The Senior Full Stack AI Engineer is responsible for designing and building advanced AI-native software systems across the full technology stack. The role combines modern software engineering, AI systems architecture, LLM integration, agentic frameworks, and AI-native development workflows.

This engineer will develop production-grade AI platforms, autonomous agents, intelligent applications, and scalable AI infrastructure while leveraging Claude Code and other AI-native development environments as core engineering tools.

The position requires a developer capable of operating within an AI-augmented engineering workflow, where complex systems are designed, built, analyzed, and optimized using advanced AI coding agents.

Core Responsibilities

AI-Native Application Development

Design and build production-grade AI applications and intelligent platforms.

Develop AI copilots, autonomous agents, and AI-driven automation systems.

Implement scalable architectures for AI-native SaaS and enterprise platforms.

Claude CodeDriven Engineering

Use Claude Code as a primary development interface for large-scale software engineering.

Architect systems using AI-assisted reasoning and repository analysis.

Develop structured prompts and engineering workflows for AI-assisted coding.

Use Claude Code for:

system architecture reasoning

automated debugging and refactoring

codebase analysis

documentation generation

automated test creation

performance optimization

Maintain AI-augmented engineering pipelines.

AI Systems & Agent Architecture

Build agentic systems and multi-agent orchestration frameworks.

Develop tool-using AI agents capable of interacting with APIs and software systems.

Design distributed AI services and reasoning pipelines.

Implement AI orchestration layers.

Technologies may include:

LangChain

LangGraph

CrewAI

AutoGen

OpenAI Assistants API

Anthropic Claude APIs

LLM Engineering

Integrate large language models into production systems.

Design and optimize prompt engineering strategies.

Implement retrieval augmented generation (RAG) architectures.

Build context management and long-context reasoning pipelines.

Implement evaluation pipelines for LLM performance.

Technologies may include:

OpenAI

Anthropic

Hugging Face

vLLM

TGI

Ollama

Knowledge Systems & Vector Infrastructure

Build semantic search and knowledge retrieval systems.

Design vector embedding pipelines.

Implement knowledge indexing and AI memory layers.

Technologies may include:

Pinecone

Weaviate

Milvus

Qdrant

FAISS

ElasticSearch

AI Data & Training Pipelines

Develop pipelines for data ingestion, transformation, and model preparation.

Work with structured, semi-structured, and unstructured datasets.

Support model fine-tuning and evaluation workflows.

Technologies may include:

PyTorch

TensorFlow

Hugging Face Transformers

Ray

Dask

Airflow

Frontend Development for AI Applications

Build modern interfaces for AI-powered software.

Implement real-time chat, copilots, and AI interaction systems.

Develop dashboards and visualization systems.

Technologies may include:

React

Next.js

TypeScript

WebSockets

GraphQL

Backend & Platform Architecture

Design APIs and microservices powering AI applications.

Build scalable backend systems supporting AI workloads.

Implement event-driven and distributed system architectures.

Technologies may include:

Python

Node.js

FastAPI

Flask

Express

Kafka

Redis

gRPC

AI Infrastructure & Deployment

Deploy AI systems at scale across cloud and hybrid environments.

Manage GPU-based inference infrastructure.

Build scalable inference pipelines.

Technologies may include:

Docker

Kubernetes

Terraform

AWS / Azure / GCP

GPU orchestration frameworks

Ray Serve

AI Security & Governance

Implement secure AI development practices.

Protect against prompt injection, model abuse, and data leakage.

Implement model monitoring and governance frameworks.

Technologies may include:

Guardrails AI

Prompt security frameworks

Model monitoring tools

Required Qualifications

Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or related field.

5+ years of software engineering experience.

2+ years building production AI systems.

Must Have:

Claude Code

Deep hands-on expertise with Claude Code as a primary engineering interface, including:

AI-assisted software architecture design

Large repository analysis

AI-driven debugging

Automated test generation

Prompt-driven development workflows

Multi-file codebase manipulation

Required Technical Skills

Programming Languages

Python

JavaScript / TypeScript

AI Engineering

LLM integration

RAG architecture

AI agent frameworks

prompt engineering

model evaluation

Data & Retrieval

vector databases

embedding pipelines

semantic search systems

Infrastructure

containerization

distributed systems

cloud deployment

Preferred Advanced Skills

multi-agent AI systems

autonomous development environments

LLM fine-tuning

reinforcement learning

AI reasoning frameworks

knowledge graph integration

GPU infrastructure optimization

Key Competencies

AI-native systems thinking

strong architectural design capability

ability to translate AI research into production systems

strong debugging and performance optimization skills

Success Metrics

speed and quality of AI-powered development

successful deployment of scalable AI systems

effectiveness of AI-assisted engineering workflows

reliability and performance of AI infrastructure