Oteemo is an industry-leading technology consulting firm at the forefront of cloud native, enterprise DevSecOps, and AI-driven transformation. We build intelligent, automated, secure systems for organizations tackling their toughest technical challenges and we''re pushing the boundaries of what AI, generative AI, and agentic systems can do in production, not just in theory. Join us and you''ll work alongside recognized experts on cutting-edge projects that blend cloud native architecture, extreme automation, and AI/ML at the core. We foster a dynamic, inclusive, and collaborative culture built on continuous learning, where your ideas shape real outcomes for our clients. If you''re passionate about building what''s next in AI and cloud technology and want to do it with a team that sets the standard rather than follows it, Oteemo is where you belong.
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:
- 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.
- 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.
- Proven 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 managemen.
- Proven expertise with RAG systems, including 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 its AI services.
- Experience building CI/CD pipelines for ML/AI application.
- 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.
- Proficiency with Git workflows, code review practices, and collaborative development.
- Excellent debugging and problem-solving skills.
- Clear technical communication and documentation abilities.
All your information will be kept confidential according to EEO guidelines.