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Accellor

Gen AI Engineer

Accellor

. Develop features, resolve defects, refactor applications, and create automated tests using AI-assisted development tools .

Posted 10/16/2026full-timeSan Francisco • California • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit
Core Competencies

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

Demonstrates proficiency in software engineering and AI development, with strong skills in prompt engineering, automated testing, and collaborative workflows. Capable of managing code quality, delivery readiness, and optimizing AI-assisted development processes.

Highest-signal resume keywords
Proficiency In Python, TypeScript, JavaScript, Java, Go, Or C#Hands-On Experience With Cursor Or Comparable AI Coding EnvironmentsStrong Prompt Engineering SkillsWorking Knowledge Of Git, Pull Requests, Debugging, Automated Testing, APIs, And CI/CDExperience Developing Workflows With Multiple Coding Agents

ATS Keywords

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Applicant Tracking System Keywords

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

Hard Skills
Software EngineeringAI EngineeringPrompt EngineeringAutomated TestingDebuggingTask DecompositionContext PreparationModel APIsAgent Orchestration FrameworksCode Review
Soft Skills
Clear CommunicationCollaborative Working HabitsOwnership Of Engineering Outcomes
Tools & Technologies
CursorOpenAI ModelsAnthropic ClaudeCI/CDCloud PlatformsContainersDeployment AutomationApplication Observability
Industry Keywords
AI-Assisted DevelopmentSoftware DevelopmentEngineering ProductivityCost EfficiencyModel Context Protocol (MCP)

Tech Stack

Tools & technologies
CloudJavaJavaScriptPythonTypeScriptGo

About the role

Key responsibilities & impact
  • Develop features, resolve defects, refactor applications, and create automated tests using AI-assisted development tools
  • Use Cursor and other coding environments for repository exploration, implementation, debugging, testing, and code review
  • Translate product requirements into engineering tasks, technical approaches, and acceptance criteria
  • Own code quality and delivery readiness, including AI-generated changes
  • Collaborate with product, engineering, and quality teams to deliver reliable software releases
  • Design and maintain prompts, repository instructions, context strategies, and reusable software-engineering workflows
  • Establish repeatable evaluations for correctness, consistency, requirement coverage, and human-intervention needs
  • Improve prompts and workflows using test results, code reviews, and delivery outcomes
  • Build coordinated coding-agent groups for planning, implementation, testing, review, and documentation
  • Design task delegation, handover, takeover, and recovery workflows with clear ownership and shared task state
  • Manage parallel execution, dependencies, isolated workspaces, and integration conflicts
  • Define tool access, execution boundaries, and human review checkpoints
  • Ensure agent-produced changes are traceable, reviewable, tested, and integrable
  • Select models and tools based on task requirements, quality, latency, and cost
  • Optimize context size, token consumption, model routing, retries, and agent overhead
  • Measure development throughput, delivery time, review effort, and AI expenditure
  • Report weekly and monthly delivery trends, including pull requests raised and merged
  • Share validated practices and reusable tools with the wider engineering team

Requirements

What you’ll need
  • 0–4 years of relevant experience in software engineering, AI engineering, or applied AI development; academic, personal, and open-source projects will also be considered
  • Proficiency in at least one programming language, such as Python, TypeScript/JavaScript, Java, Go, or C#
  • Hands-on experience applying frontier models, including OpenAI models or Anthropic Claude, to software development
  • Practical experience with Cursor or comparable AI coding environments
  • Strong prompt engineering skills, including task decomposition, context preparation, structured outputs, and iterative evaluation
  • Working knowledge of Git, pull requests, debugging, automated testing, APIs, and CI/CD
  • Ability to understand existing codebases and independently assess the correctness of AI-generated code
  • Clear communication, collaborative working habits, and ownership of engineering outcomes
  • Preferred: experience developing workflows with multiple coding agents, including delegation, handover, and integration
  • Preferred: familiarity with model APIs, tool calling, Model Context Protocol (MCP), and agent orchestration frameworks
  • Preferred: experience evaluating prompts and models using defined datasets, acceptance criteria, and automated checks
  • Preferred: experience monitoring AI usage, engineering productivity, and cost efficiency
  • Preferred: exposure to cloud platforms, containers, deployment automation, and application observability
  • Preferred: portfolio of working software demonstrating effective use of AI tools, supported by tests and technical documentation