Full Stack Engineer - Enterprise AI Applications

Oteemo, Inc•Richmond, VA

52d

About The Position

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.

Requirements

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
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
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
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

Nice To Haves

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
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
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)

Responsibilities

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
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
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