Full Stack Engineer Enterprise AI

Position: Full Stack Engineer Enterprise AI Applications

Job Description

Overview

We're seeking an exceptional Full Stack Engineer to build and scale our enterprise AI applications. You'll design and implement complete AI-powered features from database to UI, working with cutting‑edge LLM technology, RAG systems, and production ML infrastructure. This role combines full‑stack development expertise with hands‑on AI/ML engineering, deploying intelligent systems that deliver real business value at scale.

You’ll be a key technical contributor, shipping production‑ready AI features that users love while ensuring reliability, performance, and cost‑effectiveness. This is an opportunity to work at the intersection of software engineering and artificial intelligence, solving complex problems with modern technology.

What You’ll Build AI‑Powered Applications

• Design and implement end‑to‑end RAG (Retrieval‑Augmented Generation) pipelines that enable intelligent document search and question‑answering across enterprise knowledge bases
• Build production‑ready integrations with leading LLMs (GPT‑4, Claude, Gemini) that provide accurate, contextual responses to user queries
• Develop sophisticated prompt engineering strategies and evaluation frameworks to ensure consistent, high‑quality AI outputs
• Create agent systems with tool integration capabilities that can autonomously complete complex tasks
• Implement vector search solutions using Pinecone, Weaviate, or similar technologies for semantic similarity and knowledge retrieval

Full‑Stack Features

• Build scalable backend services using Python/FastAPI with type‑safe APIs, authentication, and robust error handling
• Develop responsive, performant frontend applications using React/Next.js with real‑time streaming for LLM responses
• Design and optimize database schemas spanning Postgre
  
  SQL, Mongo
  
  DB, and Redis to support high‑throughput AI workloads
• Implement Web Socket servers and event‑driven architectures for real‑time user experiences
• Create comprehensive testing strategies covering unit, integration, and end‑to‑end tests

Production Infrastructure

• Deploy and manage ML/AI services using Docker containers and Kubernetes orchestration
• Build and maintain CI/CD pipelines that enable rapid, safe deployment of AI features
• Implement infrastructure as code using Terraform to manage cloud resources (AWS, Azure, or GCP)
• Set up comprehensive monitoring and observability using Datadog, Prometheus/Grafana, and LLM‑specific tools (Lang Smith, Weights & Biases)
• Optimize costs through intelligent caching, batching strategies, and model selection algorithms
• Ensure enterprise‑grade security with proper authentication, authorization, secrets management, and compliance measures

Required Experience & Skills Full‑Stack Development (4+ years)

• Expert‑level proficiency in Python with modern frameworks (FastAPI, Flask)
• Strong Type Script/JavaScript skills with deep React and Next.js experience
• Proven track record designing and building RESTful and Graph
  
  QL APIs
• Solid understanding of relational (Postgre
  
  SQL, MySQL) and No
  
  SQL (Mongo
  
  DB) databases
• Experience with authentication systems (OAuth2, JWT, SSO) and security best practices
• Track record of shipping high‑quality, scalable software to production

AI/ML Engineering (3+ years)

• Hands‑on experience building and deploying AI/ML applications in production environments
• Deep understanding of LLM integration, prompt engineering, and context management
• Proven expertise with RAG systems: document processing, chunking, embedding, retrieval, and generation
• Experience working with vector databases (Pinecone, Weaviate, Chroma, FAISS, or Qdrant)
• Strong grasp of semantic search, similarity algorithms, and hybrid search techniques
• Knowledge of evaluation frameworks for assessing AI system quality and performance

MLOps & Infrastructure (3+ years)

• Production experience with Docker containerization and Kubernetes orchestration
• Strong knowledge of at least one major cloud platform (AWS, Azure, or GCP) and their AI services
• Experience building CI/CD pipelines for ML/AI applications
• Proficiency with infrastructure as code tools (Terraform, Cloud Formation, Pulumi)
• Understanding of monitoring, logging, and alerting best practices
• Cost optimization experience for…