Who we are
Octopus by RTG is enabling a key partner organization to grow their tech teams while focusing on AI. We are currently looking for the right pioneers to join the team!
Octopus is proud to be part of the Robusta Technology Group (RTG), a leading tech group. With a decade of experience and a successful track record of delivering over 300 projects across Europe, the Middle East, and North America, RTG has established itself as a preferred employer in the Egyptian market. Octopus and Robusta are building a bridge between Europe and Africa, creating tailored hub solutions to connect companies with top talent across the globe.
Octopus is specialized in rapidly assembling remote & onsite global tech teams that are fully aligned with the culture and practices of a particular brand. By providing tailored hubs to suit its clients’ needs, Octopus gives companies all the advantages of remote work and offshoring without all the negatives.
Role Summary
A Senior Software Engineer with 6+ years of experience building AI-powered applications. Works under the Principal Data & AI Delivery Lead to translate strategy into robust software solutions. Responsible for designing and shipping model-backed features, high-quality APIs, and user-facing services with strong security, reliability, and performance. Experience in public sector or other regulated domains is a plus.
Core Responsibilities
- 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.
Requirements
- 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.
Skills & Competencies
- 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.
Education & Credentials
- 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).
