Senior AI Engineer
New
R
RearcAI/ML engineering
Remote (USA)Full-TimeSenior
Salary$95K - $209K; $95K – $209K • $9.5K – $20.9K Bonus • Multiple Ranges; Lead Engineer (X6+): Base Salary $191K – $209K • $19.1K – $20.9K Bonus; Senior Engineer (E4-E5): Base Salary $145K – $191K • $14.5K – $19.1K Bonus; Engineer (E2-E3): Base Salary $95K – $145K • $9.5K – $14.5K Bonus
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Job Details
- Experience
- 4+ years of experience building and deploying AI/ML systems in production
- Required Skills
- DockerPythonCI/CDRESTful APIs
Requirements
- Have 4+ years of experience building and deploying AI/ML systems in production.
- Have a track record of architecting, building, and shipping AI/ML or software solutions using modern AI-assisted workflows.
- Understand AI system evaluation, including offline metrics, online monitoring, LLM-as-judge processes, regression testing, and cost and latency tracking.
- Use practical judgment to choose between RAG, agent loops, and workflows.
- Have hands-on experience with LLM platforms such as OpenAI, Anthropic, or Google Vertex, and orchestration or harness patterns.
- Be proficient in Python.
- Have backend engineering skills building and deploying APIs, working with Docker, and navigating cloud-native environments.
- Write maintainable, production-grade code.
- Have experience with CI/CD pipelines, infrastructure as code, and production observability.
- Be able to debug and optimize systems in production.
- Explain technical trade-offs to non-technical stakeholders.
- Preferred: experience with prompt optimization or evaluation tools such as DSPy, MLflow, promptfoo, or RAGAS; LLMOps/MLOps; Databricks; cloud platforms; FastAPI, Pydantic, PostgreSQL, MySQL, or DuckDB; Claude SDK or OpenAI SDK; TypeScript or Go; or mentoring 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 integrated with business workflows.
- Integrate AI systems into existing enterprise platforms and APIs.
- Debug and optimize live production systems.
- Collaborate with client and internal engineering teams.
- Participate in technical design discussions focused on implementation.
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