Applied AI Engineer
Position Overview
We are seeking an Applied AI Engineer with a strong software engineering background to join our AI initiative team, focusing on building production LLM applications and implementing cutting-edge AI solutions. This role requires hands-on coding expertise across full-stack development with emphasis on LLM/RAG systems.
Key Responsibilities
Build production LLM applications using FastAPI/Spring Boot backends with React/Next.js frontends
Implement and optimize RAG systems with vector databases (Pinecone, Weaviate, OpenSearch)
Develop reusable LLM components (prompt engineering, chain orchestration, response streaming)
Create REST/GraphQL APIs for AI services integration
Deploy containerized AI applications on AWS (ECS/EKS, Lambda)
Assess and evaluate AI tools, making strategic build vs. buy recommendations
Implement caching, rate limiting, and guardrails for LLM APIs
Required Technical Skills
Programming Languages & Frameworks
Python : FastAPI, Flask, Django (3+ years required)
Java : Spring Boot, Spring Security, Spring Data
JavaScript : React, Next.js, Node.js
Strong API development and microservices architecture
AI/ML Frameworks & Tools (Required)
RAG Implementation : MUST HAVE - Production experience with RAG pipelines
LangChain/LlamaIndex : Building AI applications and chains
Vector Databases : Pinecone, Weaviate, OpenSearch
Embedding Models : OpenAI, Cohere, Sentence Transformers
Data Science:
Foundation knowledge
Development Tools (Required)
Cursor
OR
Windsurf : AI-powered development environments
GitHub Copilot/CodeWhisperer : AI productivity tools
AWS Cloud Platform (Required)
AWS Bedrock : Managed foundation models
Prompt Engineering : Advanced prompt design and optimization
AI Guardrails : Safety and content filtering mechanisms
Additional: Lambda, ECS/EKS, API Gateway, S3, DynamoDB
Experience Requirements
3-5 years in
software engineering
eCommerce experience
strongly preferred (product search, chatbots, personalization)
Production web application development
Agile/DevOps practices
What You'll Work On
eCommerce AI features: conversational commerce, semantic search, content generation
API-first AI microservices following best practices
POCs demonstrating AI capabilities and ROI
Technical documentation and implementation guide