Senior AI Software Engineer Onsite In Riyadh- Octopus by RTG
- Riyadh
- Permanent
- Full-time
- Delivery Execution: Design and implement backend services and APIs that integrate AI capabilities; apply suitable architectural patterns; follow coding standards with peer reviews.
- Model Serving & Inference: Package models as services, optimize latency and throughput, and apply safe rollout strategies such as canary releases and A/B testing.
- Retrieval & Context: Enable semantic search and assistant features using embeddings, vector search, and prompt orchestration where appropriate.
- Client Integration: Define clear API contracts and lightweight SDKs; collaborate with web and mobile teams to deliver end-to-end features.
- Security & Privacy: Implement authentication, authorization, encryption at rest and in transit, secrets management, and responsible handling of personal data; align with governance controls.
- Observability & Reliability: Instrument services with metrics, logs, and traces; define service objectives and alerts; participate in incident response and post-incident reviews.
- Continuous Delivery & Platform Engineering: Use containerization, orchestration, infrastructure as code, and automated pipelines for build, test, and deployment with environment promotion.
- Testing: Write unit, integration, contract, and performance tests; mock or stub model endpoints to ensure repeatable test runs.
- Documentation & Knowledge Sharing: Produce technical documentation, diagrams, and runbooks; contribute to reusable components, patterns, and templates.
- Pilot & Scale Support: Support controlled pilots, gather feedback, harden services for production scale, and hand over cleanly to operations.
- 6+ years in software engineering, including 3+ years building cloud services at scale.
- 2+ years implementing AI capabilities in production (e.g., large language model features, NLP, computer vision, recommendation systems).
- Experience training and serving machine learning models using industry-standard tools and practices.
- Proven integration of foundation models via managed services or self-hosted deployments.
- Experience with retrieval-augmented generation (RAG) patterns, vector databases, and embeddings.
- Practical use of relational and non-relational datastores, caching, and event streaming or messaging platforms.
- Cloud platform experience, containerization and orchestration, and CI/CD with Git-based workflows.
- Security-first development practices, identity and access management.
- Familiarity with regulated or public sector environments is a plus.
- Software Design & Architecture: Clean code, API-first design, modular architectures, and appropriate service boundaries.
- AI Product Thinking: Translate model capabilities into reliable user features, set guardrails and fallbacks, monitor quality and safety.
- Performance Engineering: Profiling, caching, batching, and concurrency to meet latency and throughput targets.
- Quality Mindset: Automated testing, code reviews, static analysis, and continuous improvement.
- Delivery Skills: Estimation, task breakdown, and agile ways of working with clear status reporting.
- Communication & Collaboration: Clear documentation, close partnership with Product, Security, and QA teams, and mentoring of junior engineers.
- Problem-Solving: Structured analysis, prioritization, and proactive risk management.
- Bachelor’s degree in Computer Science, Software Engineering, or related field (or equivalent practical experience).
- Relevant certifications are a plus (e.g., cloud provider certifications, container orchestration, applied machine learning).