At Rearc, we're committed to empowering engineers to build awesome products and experiences. Success as a business hinges on our people's ability to think freely, challenge the status quo, and speak up about alternative problem-solving approaches. If you're driven by the desire to solve problems and make a difference, you're in the right place!
Our approach is simple: empower our people with the best tools possible to make an impact within their industry. We're on the lookout for people who thrive on ownership and freedom, possessing not just technical depth but also executive presence and business judgment.
Founded in 2016, we pride ourselves on fostering an environment where creativity flourishes, bureaucracy is minimal, and individuals are encouraged to challenge the status quo. We're not just a company; we're a community of problem-solvers dedicated to improving the lives of fellow software engineers and the customers we serve.
Role Overview
Rearc is seeking a hands-on AI Engineer to design, build, and deploy production-grade AI-driven systems within enterprise environments. This position is focused on delivering real-world AI/ML solutions that address actual customer needs—not prototypes, notebooks, or one-off scripts generated by tools without deep understanding. As an AI Engineer at Rearc, you will work across the full development lifecycle, from system design to production deployment, building robust AI-powered applications that integrate into business workflows and deliver measurable impact. This is a 100% hands-on engineering role.
We value your 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 processes.
What You Bring
- 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
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 (e.g., 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
What You'll Do
- 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
Rearc is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees, regardless of race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. All employment decisions at Rearc are based on business needs, job requirements, and individual qualifications.
