Applied AI engineer
4 days ago
Englewood Cliffs
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 β’ Build production LLM applications using FastAPI/Spring Boot backends with React/Next.js frontends, β’ Implement and optimize RAG systems with vector databases (Pinecone, Weaviate, OpenSearch), β’ Develop reusable LLM components (prompt engineering, chain orchestration, response streaming), β’ Create REST/GraphQL APIs for AI services integration, β’ Deploy containerized AI applications on AWS (ECS/EKS, Lambda), β’ Assess and evaluate AI tools, making strategic build vs. buy recommendations, β’ Implement caching, rate limiting, and guardrails for LLM APIs Required Technical Skills Programming Languages & Frameworks β’ Python: FastAPI, Flask, Django (3+ years required), β’ Java: Spring Boot, Spring Security, Spring Data, β’ JavaScript: React, Next.js, Node.js, β’ RAG Implementation: MUST HAVE - Production experience with RAG pipelines, β’ LangChain/LlamaIndex: Building AI applications and chains, β’ Vector Databases: Pinecone, Weaviate, OpenSearch, β’ Embedding Models: OpenAI, Cohere, Sentence Transformers, β’ Cursor OR Windsurf: AI-powered development environments, β’ AWS Bedrock: Managed foundation models, β’ Prompt Engineering: Advanced prompt design and optimization, β’ AI Guardrails: Safety and content filtering mechanisms, β’ 3-5 years in software engineering, β’ eCommerce experience strongly preferred (product search, chatbots, personalization), β’ Production web application development, β’ Agile/DevOps practices What You'll Work On β’ eCommerce AI features: conversational commerce, semantic search, content generation, β’ API-first AI microservices following best practices, β’ POCs demonstrating AI capabilities and ROI, β’ Technical documentation and implementation guide