AI Engineer

AI Engineering is all about making AI a reality in production, not just prototyping. It means building the frameworks and platforms that allow Dojo to prototype AI solutions quickly and then take the best of those into production with the right guardrails, scalability, resilience, and reliability in place.

We’re not here just to experiment. We’re here to make AI usable, safe, and impactful across the business by combining strong engineering with practical AI know‑how. That means giving teams the tools to prototype fast and learn, creating the platform and infrastructure to run AI solutions at scale, embedding security, monitoring, and governance so AI can be trusted, and designing for resilience and performance so solutions last, not just launch.

• Design and build the AI platform that powers LLMs, agents, and other AI solutions across Dojo.
• Develop APIs, SDKs, and tooling that allow product teams to consume AI capabilities at scale while having a great developer experience.
• Implement orchestration for multi‑model and multi‑service workflows (e.g., Lang Graph, Crew AI, Google Agent Development Kit, agentic frameworks).
• Build and manage vector search and retrieval systems to support RAG and knowledge integration.
• Build robust monitoring, logging, and guardrails to ensure AI systems are safe, observable, and compliant using solutions like Langsmith, Opik, Prometheus, and Grafana.
• Automate infrastructure and model deployment with Kubernetes, Terraform, and CI/CD pipelines.
• Partner with security, compliance, and product to ensure safe use of AI in production.
• Stay on top of AI platform trends, open‑source tools, and emerging patterns — bringing best practices into our stack.

• Strong software or platform engineering background (Python/Go/Java; Go/Java/.NET a bonus).
• Solid experience with distributed systems, microservices, and cloud‑native architecture (GCP preferred).
• Hands‑on experience with Kubernetes, service mesh, and event‑driven systems.
• Familiarity with LLM orchestration frameworks (Lang Chain, Lang Graph, CrewAI, GCP ADK or similar).
• Experience with vector databases (FAISS, Pinecone, Weaviate, Vertex Vector Search) and RAG pipelines.
• Knowledge of MLOps/AI infra tools (MLflow, Vertex AI, Ollama, Open Router, etc.).
• Strong CI/CD and infrastructure‑as‑code skills (Terraform, Helm, etc.).
• Good understanding of AI governance, monitoring, and responsible AI practices.
• Comfort balancing speed (PoCs) with robustness (production‑ready systems).