About
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. 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 PostgreSQL, MongoDB, and Redis to support high-throughput AI workloads Implement WebSocket 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 (LangSmith, 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 TypeScript/JavaScript skills with deep React and Next.js experience Proven track record designing and building RESTful and GraphQL APIs Solid understanding of relational (PostgreSQL, MySQL) and NoSQL (MongoDB) 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, CloudFormation, Pulumi) Understanding of monitoring, logging, and alerting best practices Cost optimization experience for cloud and AI workloads Software Engineering Excellence Strong computer science fundamentals and algorithmic thinking Experience with test-driven development (TDD) and comprehensive testing strategies Proficiency with Git workflows, code review practices, and collaborative development Excellent debugging and problem-solving skills Clear technical communication and documentation abilities Agile/Scrum experience with ability to work in fast-paced environments Preferred Qualifications Advanced AI Capabilities Experience with LangChain, LlamaIndex, LangGraph, or similar LLM frameworks Knowledge of fine-tuning techniques (LoRA, QLoRA) and parameter-efficient methods Familiarity with agent architectures, tool-using systems, and Model Context Protocol (MCP) Experience with multi-modal AI (vision-language models, document understanding) Background in prompt optimization, structured outputs, and function calling Extended Technical Skills Additional programming languages: Go, Rust, or Node.js/TypeScript backend experience Advanced Kubernetes knowledge: Helm, operators, service mesh (Istio) Experience with message queues (Kafka, RabbitMQ, AWS SQS) and event-driven architectures Knowledge of graph databases (Neo4j) for advanced memory systems Contributions to open-source AI/ML projects Leadership & Collaboration Experience mentoring junior engineers and conducting technical interviews Track record of making impactful architectural decisions Ability to translate complex technical concepts for non-technical stakeholders Experience working across teams (product, design, data science) Additional Information All your information will be kept confidential according to EEO guidelines.
Languages
- English
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