Senior AI Engineer

Rearc is building production-grade, enterprise-ready AI/ML systems, and this role is a 100% hands-on engineering position focused on shipping real outcomes. You’ll design, build, and deploy AI-driven capabilities that go beyond prototypes, with an emphasis on end-to-end delivery: evaluation, monitoring, and integration into business workflows.

Location: Remote
Compensation: USD 95,000 - 209,000 per year
Experience: 4+ years

What you’ll do

• 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 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 client and internal engineering teams
• Participate in technical design discussions with a focus on implementation

What you bring

• 4+ years building and deploying AI/ML systems in production (beyond demos or experimentation)
• A 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: 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 such as OpenAI, Anthropic, Google Vertex, or similar, plus orchestration/harness patterns
• Python proficiency, forming the foundation of engineering work
• Backend engineering skills including building and deploying APIs, working with Docker, and navigating cloud-native environments (containers and basic infrastructure)
• Strong software engineering fundamentals: production-grade, maintainable code
• Experience with CI/CD pipelines, infrastructure as code, and production observability
• Ability to debug and optimize systems already in production
• Strong communication skills, including explaining technical trade-offs to non-technical stakeholders

Technologies you may work with

Python, OpenAI, Anthropic, Google Vertex, Docker, CI/CD pipelines, infrastructure as code, LLM-as-judge processes, RAG, FastAPI, Pydantic, PostgreSQL, MySQL, DuckDB, DSPy, MLflow, promptfoo, RAGAS, Claude SDK, OpenAI SDK, TypeScript, Go, Databricks, AWS, Azure, GCP

Preferred experience

• 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 (for example, developing on/with Goose, Pi, Claude Code, Codex)
• Databricks experience (preferred)
• 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