Full Stack Engineer - Enterprise AI Applications

OverviewWe'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 BuildAI-Powered ApplicationsDesign and implement end-to-end RAG (Retrieval-Augmented Generation) pipelines that enable intelligent document search and question-answering across enterprise knowledge basesBuild production-ready integrations with leading LLMs (GPT-4, Claude, Gemini) that provide accurate, contextual responses to user queriesDevelop sophisticated prompt engineering strategies and evaluation frameworks to ensure consistent, high-quality AI outputsCreate agent systems with tool integration capabilities that can autonomously complete complex tasksImplement vector search solutions using Pinecone, Weaviate, or similar technologies for semantic similarity and knowledge retrievalFull-Stack FeaturesBuild scalable backend services using Python/FastAPI with type-safe APIs, authentication, and robust error handlingDevelop responsive, performant frontend applications using React/Next.js with real-time streaming for LLM responsesDesign and optimize database schemas spanning PostgreSQL, MongoDB, and Redis to support high-throughput AI workloadsImplement WebSocket servers and event-driven architectures for real-time user experiencesCreate comprehensive testing strategies covering unit, integration, and end-to-end testsProduction InfrastructureDeploy and manage ML/AI services using Docker containers and Kubernetes orchestrationBuild and maintain CI/CD pipelines that enable rapid, safe deployment of AI featuresImplement 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 (LangSmith, Weights & Biases)Optimize costs through intelligent caching, batching strategies, and model selection algorithmsEnsure enterprise-grade security with proper authentication, authorization, secrets management, and compliance measuresRequired Experience & SkillsFull-Stack Development (4+ years) Expert-level proficiency in Python with modern frameworks (FastAPI, Flask)Strong TypeScript/JavaScript skills with deep React and Next.js experienceProven track record designing and building RESTful and GraphQL APIsSolid understanding of relational (PostgreSQL, MySQL) and NoSQL (MongoDB) databasesExperience with authentication systems (OAuth2, JWT, SSO) and security best practicesTrack record of shipping high-quality, scalable software to productionAI/ML Engineering (3+ years)Hands-on experience building and deploying AI/ML applications in production environmentsDeep understanding of LLM integration, prompt engineering, and context managementProven expertise with RAG systems: document processing, chunking, embedding, retrieval, and generationExperience working with vector databases (Pinecone, Weaviate, Chroma, FAISS, or Qdrant)Strong grasp of semantic search, similarity algorithms, and hybrid search techniquesKnowledge of evaluation frameworks for assessing AI system quality and performanceMLOps & Infrastructure (3+ years)Production experience with Docker containerization and Kubernetes orchestrationStrong knowledge of at least one major cloud platform (AWS, Azure, or GCP) and their AI servicesExperience building CI/CD pipelines for ML/AI applicationsProficiency with infrastructure as code tools (Terraform, CloudFormation, Pulumi)Understanding of monitoring, logging, and alerting best practicesCost optimization experience for cloud and AI workloadsSoftware Engineering ExcellenceStrong computer science fundamentals and algorithmic thinkingExperience with test-driven development (TDD) and comprehensive testing strategiesProficiency with Git workflows, code review practices, and collaborative developmentExcellent debugging and problem-solving skillsClear technical communication and documentation abilitiesAgile/Scrum experience with ability to work in fast-paced environments