Rearc is hiring a hands-on Senior AI Engineer to design, build, and deploy production-grade AI/ML systems for enterprise environments.

Design and implement AI agents, including RAG pipelines, orchestration workflows, and tool invocation
Build evaluation frameworks to measure accuracy, latency, cost, and reliability
Implement observability and monitoring across the AI system lifecycle
Integrate with multiple AI providers and create abstraction layers for multi-model architectures
Optimize AI systems for performance, cost, and scalability
Build and deploy AI-powered applications tied to real business workflows
Integrate AI systems into existing enterprise platforms and APIs
Debug and optimize live production AI/ML systems
Collaborate with both client and internal engineering teams
Participate in technical design discussions with an implementation-focused approach

4+ years of experience building and deploying AI/ML systems in production (not demos or experimentation)
Demonstrated ability to architect, build, and ship AI/ML or software solutions using modern AI-assisted workflows
Strong knowledge of AI system evaluation and measurement, including offline metrics, online monitoring, LLM-as-judge processes, regression testing, and cost/latency tracking
Practical judgment for retrieval and agent design trade-offs, including choosing between RAG, agent loops, and workflows as needed
Hands-on experience with LLM platforms such as OpenAI, Anthropic, Google Vertex, or similar, plus orchestration/harness patterns
Proficiency in Python
Backend engineering experience building and deploying APIs, working with Docker, and navigating cloud-native environments (containers and basic infrastructure)
Strong software engineering fundamentals: production-grade, maintainable code (not just wiring demos)
Experience with CI/CD pipelines, infrastructure as code, and production observability
Ability to debug and optimize systems already running in production
Strong communication skills, including explaining technical trade-offs to non-technical stakeholders

Familiarity with prompt optimization or evaluation tools such as DSPy, MLflow, promptfoo, RAGAS, etc.
LLMOps/MLOps experience: building robust, monitored, self-healing AI systems
Experience with harness engineering (examples: Goose, Pi, Claude Code, Codex)
Databricks experience
Experience with cloud platforms including AWS, Azure, or GCP
Experience using FastAPI, Pydantic, PostgreSQL, MySQL, or DuckDB
Experience with the Claude SDK or OpenAI SDK
Additional programming languages beyond Python (for example, TypeScript or Go)
Experience mentoring or upskilling fellow engineers

100% hands-on engineering role focused on designing, building, deploying, and continually improving AI systems end-to-end
Emphasis on real-world AI/ML solutions that address customer needs, not prototypes, notebooks, or one-off scripts
Focus on shipping and evaluating AI systems rather than proprietary model training and fine-tuning processes

Senior AI Engineer

Job Description