Full Stack AI Engineer (Generative AI)

Appiness Inc.
yesterday

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English

Job location

Remote

Tech stack

Java
JavaScript
.NET
API
Artificial Intelligence
Amazon Web Services (AWS)
Software Applications
JIRA
HTML5
Azure
Microsoft Online Services
CSS
Software as a Service
Databases
DevOps
Django
Github
Python
PostgreSQL
Microsoft SQL Server
MongoDB
MySQL
Node.js
Open Source Technology
Redis
Next.js
Salesforce
Search Technologies
SharePoint
Systems Integration
TypeScript
Google Cloud Platform
Enterprise Software Applications
.NET Core
Microsoft Power Automate
Spring Cloud
Tailwind
React
Flask
Large Language Models
Snowflake
Grafana
Multi-Agent Systems
Prompt Engineering
Spring-boot
Generative AI
FastAPI
Microsoft Fabric
Containerization
Angular
Material Design
Kubernetes
Low Latency
GraphQL
Front End Software Development
Virtual Agents
Api Design
NestJS
REST
Terraform
GPT
Automation Anywhere
Docker
ServiceNow
Databricks
Microservices

Job description

We are seeking a highly skilled Full Stack AI Engineer to design, develop, and deploy next-generation AI-powered enterprise applications. The ideal candidate will have strong experience in full-stack software development combined with expertise in Generative AI, AI Agents, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs).

You will work closely with product managers, architects, and business stakeholders to build intelligent applications that automate workflows, enhance productivity, and deliver exceptional user experiences., * 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., * Python (FastAPI, Flask, Django)
  • Java (Spring Boot) or
  • .NET Core ( Core) or
  • 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

  • Pinecone

  • Weaviate

  • Milvus

  • Chroma

  • FAISS

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)

Requirements

Frontend

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

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