Full Stack AI Engineer (Gen AI) | W2 Only | 100% Remote

Key Responsibilities

• Design, develop, and maintain scalable full-stack web applications.
• Build AI-powered applications leveraging Large Language Models (LLMs) such as OpenAI GPT, Anthropic Claude, Google Gemini, and open-source models.
• Develop intelligent AI Agents capable of planning, reasoning, and executing multi-step tasks.
• Design and implement Retrieval-Augmented Generation (RAG) architectures using enterprise data sources.
• Integrate AI capabilities into existing Java, .NET, or Node.js enterprise applications.
• Develop RESTful APIs and microservices using Python (FastAPI/Flask) or Node.js.
• Build responsive frontend applications using React.js, Next.js, or Angular.
• Implement vector search solutions using Pinecone, Weaviate, ChromaDB, Milvus, or FAISS.
• Optimize prompts, embeddings, context retrieval, and AI workflows for accuracy and performance.
• Integrate enterprise systems including Microsoft Graph, Salesforce, ServiceNow, Jira, SharePoint, and other SaaS platforms.
• Develop secure, scalable cloud-native applications on AWS, Azure, or Google Cloud Platform.
• Containerize applications using Docker and deploy through Kubernetes and CI/CD pipelines.
• Monitor AI application performance, latency, token usage, and model quality.
• Follow AI governance, responsible AI, and security best practices.

Required Skills

Frontend

Backend

Generative AI

AI Frameworks

RAG & Search

Databases

Cloud & DevOps

Frontend

• React.js / Next.js
• TypeScript / JavaScript
• HTML5, CSS3
• Tailwind CSS / Material UI

Backend

• Python (FastAPI, Flask, Django)
• Java (Spring Boot), .NET Core (Core), Node.js (Express/NestJS)
• REST APIs
• GraphQL (preferred)

Generative AI

• OpenAI API
• Anthropic Claude API
• Google Gemini
• Azure OpenAI
• Prompt Engineering
• Function Calling
• Structured Outputs
• AI Agent Development
• Multi-Agent Systems
• Model Context Protocol (MCP)

AI Frameworks

• LangChain
• LangGraph
• LlamaIndex
• CrewAI or AutoGen (preferred)
• Semantic Kernel (preferred)

RAG & Search

• Retrieval-Augmented Generation (RAG)
• Vector Databases
• Embeddings
• Semantic Search

Databases

• PostgreSQL
• MySQL
• SQL Server
• MongoDB
• Redis

Cloud & DevOps

• AWS / Azure / Google Cloud Platform
• Docker
• Kubernetes
• GitHub Actions
• Azure DevOps, CI/CD Pipelines, Terraform (preferred)

Preferred Qualifications

• Experience building enterprise AI Copilots and AI Assistants.
• Experience developing Agentic AI solutions.
• Hands-on experience with Microsoft Copilot Studio or Azure AI Foundry.
• Experience integrating Databricks, Snowflake, or Microsoft Fabric with AI applications.
• Familiarity with AI observability tools such as LangSmith, Arize AI, or PromptLayer.
• Knowledge of AI security, responsible AI, and model governance.
• Experience working in Agile/Scrum environments.