rearc - Senior AI Engineer
Requirements
• 4+ years of experience building and deploying AI/ML systems in production (beyond demos or experimentation) • Track record of architecting, building, and successfully shipping AI/ML or software solutions using modern AI-assisted workflows • Strong understanding of AI system evaluation and measurement: including offline metrics, online monitoring, LLM-as-judge processes, regression testing, and cost/latency tracking • Practical judgment in retrieval and agent design trade-offs, with the ability to explain and choose between RAG, agent loops, and workflows as needed • Hands-on experience with LLM platforms (OpenAI, Anthropic, Google Vertex, or similar) and orchestration/harness patterns • Proficiency in Python, which forms the foundation of our engineering efforts • Solid backend engineering skills: building and deploying APIs, working with Docker, and navigating cloud-native environments (containers, basic infrastructure) • Strong software engineering fundamentals: you write maintainable, production-grade code, not just wire together demos • Experience with CI/CD pipelines, infrastructure as code, and production observability • Ability to debug and optimise systems already in production • Strong communication skills, including the ability to explain technical trade-offs to non-technical stakeholders • Familiarity with prompt optimization or evaluation tools (DSPy, MLflow, promptfoo, RAGAS, etc.) • LLMOps/MLOps experience: building robust, monitored, self-healing AI systems • Experience with harness engineering (e.g., developing on/with Goose, Pi, Claude Code, Codex) • Experience with cloud platforms such as AWS, Azure, or GCP • Experience with FastAPI, Pydantic, PostgreSQL, MySQL, or DuckDB • Experience using the Claude SDK or OpenAI SDK • Additional programming languages beyond Python—TypeScript or Go are strong positives • Experience mentoring or upskilling fellow engineers
Responsibilities
• Design and implement AI agents, including RAG pipelines, orchestration workflows, and tool invocation • Build evaluation frameworks to measure system accuracy, latency, cost, and reliability • Implement observability and monitoring across the AI system lifecycle • Integrate with multiple AI providers and develop abstraction layers for multi-model architectures • Optimize AI systems for performance, cost, and scalability • Build and deploy AI-powered applications tightly coupled with real business workflows • Integrate AI systems into existing enterprise platforms and APIs • Debug and optimize live production systems • Collaborate closely with both client and internal engineering teams • Participate in technical design discussions with a focus on implementation
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