Senior AI Engineer

Rearc

4d•$95,000 - $209,000

About The Position

Rearc is seeking a hands-on AI Engineer to design, build, and deploy production-grade AI-driven systems in enterprise environments. The role focuses on delivering practical AI/ML solutions that meet customer needs, rather than prototypes, notebooks, or one-off scripts. The engineer will work across the full development lifecycle, from system design through production deployment, building robust AI-powered applications that integrate into business workflows and deliver measurable impact. This is a 100% hands-on engineering role. Rearc values the ability to ship, evaluate, and continually improve AI systems end-to-end, rather than deep theoretical expertise or experience with proprietary model training and fine-tuning. The role involves collaborating with client and internal engineering teams and participating in technical design discussions focused on implementation.

Requirements

At least 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 and 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, and orchestration or harness patterns.
Proficiency in Python.
Solid backend engineering skills, including building and deploying APIs, working with Docker, and navigating cloud-native environments such as containers and basic infrastructure.
Strong software engineering fundamentals and ability to write maintainable, production-grade 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 the ability to explain technical trade-offs to non-technical stakeholders.

Nice To Haves

Familiarity with prompt optimization or evaluation tools such as DSPy, MLflow, promptfoo, or RAGAS.
LLMOps/MLOps experience building robust, monitored, self-healing AI systems.
Experience with harness engineering, such as developing on or with Goose, Pi, Claude Code, or Codex.
Databricks experience.
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 client and internal engineering teams.
Participate in technical design discussions with a focus on implementation.