Senior AI Engineer

Rearc

. Design and implement AI agents, including RAG pipelines, orchestration workflows, and tool invocation .

Posted 11/1/2026full-timeRemote • United StatesSenior💰 $95,000 - $209,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and implementing AI agents and systems, with a strong focus on performance optimization, evaluation frameworks, and integration into enterprise platforms. Proficient in backend engineering and capable of collaborating effectively with technical and non-technical stakeholders.

Highest-signal resume keywords

AI/ML Systems DevelopmentPython ProficiencyProduction ObservabilityAPI DevelopmentLLM Platforms Experience

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

AI System EvaluationBackend EngineeringProduction-Grade CodingCI/CD PipelinesDockerCloud-Native EnvironmentsRAG PipelinesOrchestration WorkflowsDebugging Production SystemsCost/Lag Tracking

Soft Skills

Strong Communication SkillsTechnical Trade-Off Explanation

Tools & Technologies

OpenAIAnthropicGoogle VertexFastAPIPydanticPostgreSQLMySQLDuckDBClaude SDKOpenAI SDK

Industry Keywords

AI AgentsMulti-Model ArchitecturesMLOpsHarness EngineeringDSPyMLflowRAGASDatabricksAWSAzure

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoogle Cloud PlatformMySQLPostgresPythonTypeScriptGo

About the role

Key responsibilities & impact

Design and implement AI agents, including RAG pipelines, orchestration workflows, and tool invocation
Build evaluation frameworks measuring accuracy, latency, cost, and reliability
Implement observability and monitoring across the AI system lifecycle
Integrate 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 enterprise platforms and APIs
Debug and optimize live production systems
Collaborate with client and internal engineering teams
Participate in implementation-focused technical design discussions

Requirements

What you’ll need

4+ years of experience building and deploying AI/ML systems in production
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, including choosing between RAG, agent loops, and workflows
Hands-on experience with LLM platforms such as OpenAI, Anthropic, or Google Vertex, and orchestration/harness patterns
Proficiency in Python
Solid backend engineering skills, including building and deploying APIs, Docker, and cloud-native environments
Strong software engineering fundamentals and production-grade coding ability
Experience with CI/CD pipelines, infrastructure as code, and production observability
Ability to debug and optimize production systems
Strong communication skills and ability to explain technical trade-offs to non-technical stakeholders
Preferred: familiarity with DSPy, MLflow, promptfoo, RAGAS, or similar evaluation tools
Preferred: LLMOps/MLOps experience
Preferred: harness engineering experience with Goose, Pi, Claude Code, or Codex
Preferred: Databricks experience
Preferred: AWS, Azure, or GCP experience
Preferred: FastAPI, Pydantic, PostgreSQL, MySQL, or DuckDB experience
Preferred: Claude SDK or OpenAI SDK experience
Preferred: TypeScript or Go experience
Preferred: mentoring or upskilling experience

Benefits

Comp & perks

Bonus opportunity of $9.5K–$20.9K
Remote work arrangement