Teams at Robots & Pencils

Deploy and operate production GenAI agents, MCP servers, and ML prediction APIs in Azure and AWS environments. Responsibilities include containerization with Docker, CI/CD through GitHub Enterprise, Azure ML workspace and compute setup, access management, observability, troubleshooting, and deployment documentation. The engineer will collaborate with Data Scientists and ML Engineers, support enterprise client environments, and potentially build demand-prediction models. MLOps, data engineering, and consulting experience are preferred.

Design and deliver cloud, data, and AI/ML architectures while building cloud-native microservices, serverless and containerized systems. Implement infrastructure as code, CI/CD, data pipelines, MLOps, monitoring, logging, and observability. Collaborate across engineering, data, AI, and product teams, document architectural decisions, participate in design reviews, establish best practices, own system components end-to-end, and mentor junior engineers.

Designs, builds, and scales production AI systems, including ML platforms, inference pipelines, agentic orchestration, LLMOps, RAG, and distributed infrastructure. The role owns architecture and technical strategy, operationalizes research prototypes, establishes evaluation and deployment practices, and ensures reliability, observability, security, and cost efficiency. It also mentors engineers, leads technical reviews, collaborates with product and executive stakeholders, and guides AI initiatives from experimentation through production.