Senior AI Engineer

Role Overview

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

What You'll Do

Qualifications

• 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
• 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)
• 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.

Preferred Qualifications

• Preferred Experience
• Databricks experience (preferred)

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 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.