AI Software Engineer

Oteemo Inc.; Virginia (IL); On-site; USD 120,000 - 190,000; Full time

Job summary

Oteemo Inc. seeks an exceptional AI Software Engineer to build and scale enterprise AI applications end to end, from database to UI.

You will work with cutting‑edge LLMs, RAG systems, and production ML infrastructure to ship intelligent features that deliver real business value at scale. You will design end‑to‑end RAG pipelines, integrate with leading models, develop prompt strategies, and create agent systems.

Responsibilities

• Design and implement end-to-end RAG pipelines for intelligent document search and question-answering across enterprise knowledge bases.
• Build production-ready integrations with leading LLMs (GPT-4, Claude, Gemini) for accurate, contextual responses to user queries.
• Develop 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 across 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 for rapid, safe deployment of AI features.
• Implement infrastructure as code using Terraform to manage cloud resources (AWS, Azure, or GCP).
• Set up 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 through authentication, authorization, secrets management, and compliance measures.

Job description

We're seeking an exceptional AI Software Engineer to build and scale enterprise AI applications end to end from database to UI. In this role, you'll work with cutting-edge LLM technology, RAG systems, and production ML infrastructure, combining full-stack development expertise with hands-on AI/ML engineering to ship 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.Key Responsibilities:

Key Responsibilities:

• Design and implement end-to-end RAG (Retrieval-Augmented Generation) pipelines for intelligent document search and question-answering across enterprise knowledge bases.
• Build production-ready integrations with leading LLMs (GPT-4, Claude, Gemini) for accurate, contextual responses to user queries.
• Develop 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 across 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 for rapid, safe deployment of AI features.
• Implement infrastructure as code using Terraform to manage cloud resources (AWS, Azure, or GCP).
• Set up 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 through authentication, authorization, secrets management, and compliance measures.