AI-Native Software Engineer

Jobtailor

Arlington (VA)

On-site

USD 140,000 - 190,000

Full time

14 days+

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Job summary

Jobtailor is seeking an experienced AI-enabled software engineer to advance our generative AI workflows and tooling. You will work closely with engineering teams to embed AI copilots, testing agents, and NLP pipelines in production software, improving velocity and quality.

You will design prompt workflows, assess AI-generated code for security and performance, and help scale AI across multiple deployment environments while collaborating with security and compliance teams.

Qualifications

Bachelor's degree in Computer Science, Data Science, Engineering, or related field, or equivalent professional experience.
5-7 years of full-time professional experience.
Ability to travel up to 10% of the time, as needed.
Experience using generative AI coding tools.
Experience using LLMs for code generation, debugging, test generation, and documentation automation.
Familiarity with agent-based development workflows and AI-assisted environments.
Understanding of secure software development practices and AI-specific security risks.

Responsibilities

Use generative AI tools daily as a core part of software engineering workflows.
Demonstrate and share AI-enabled techniques to improve productivity and software quality.
Help define AI-augmented engineering standards, patterns, and best practices.
Prototype and evaluate AI developer tooling such as agents and automated testing agents.
Mentor engineering teams on effective use of generative AI in development workflows.
Integrate AI components into complex software environments.
Develop intelligent data processing and NLP pipelines.
Deliver AI-enabled dashboards, interfaces, and insights.
Identify lifecycle areas accelerated through generative AI and automation.

Skills

Generative AI

LLM code generation

AI tooling

AI debugging

Prompt design

Security in AI

Cross-functional collaboration

Mentoring teams

Education

Bachelor's degree in CS/Data Science/Engineering

Tools

GitHub Copilot

Cursor

Codeium

ChatGPT

Claude

Job description

Use generative AI tools (LLM copilots, autonomous coding agents, AI test generators) as a core part of daily software engineering workflows
Demonstrate and share AI-enabled engineering techniques that improve team productivity and software quality
Help define and evolve AI-augmented engineering standards, patterns, and best practices
Prototype and evaluate emerging AI developer tooling (agent frameworks, code copilots, automated testing agents)
Mentor engineering teams on effective use of generative AI in development workflows
Integrate AI Components into Complex DoW Software Environments
Integrate AI models and services into mission-critical software systems.
Develop Intelligent Data Processing and NLP Pipelines
Deliver AI-Enabled Dashboards, Interfaces, and Insights
Help identify areas of the software lifecycle that can be accelerated through generative AI and automation.

Requirements

Bachelor's degree in Computer Science, Data Science, Engineering, or a related field, or equivalent full-time professional experience.
5-7 years of full-time professional experience
Ability to travel up to 10% of the time, as needed
Demonstrated curiosity and experimentation with emerging AI technologies and engineering practices.
Daily use of generative AI coding tools such as GitHub Copilot, Cursor, Codeium, ChatGPT, Claude, or similar systems
Experience using LLMs for code generation, debugging, test generation, and documentation automation
Familiarity with agent-based development workflows and AI-assisted engineering environments
Experience designing prompt workflows or system prompts to improve developer productivity
Ability to critically evaluate AI-generated code for correctness, security, and performance
Strong understanding of AI concepts and practical deployment patterns
Demonstrated ability to scope requirements for engineering solutions that meet business challenges using AI with measurable outcomes
Experience in engineering solutions that integrate AI models into production workflows to improve User Experience and productivity
Understanding of security and compliance considerations when implementing AI solutions, especially in sensitive or classified environments, to support multiple deployment environments
Understanding of secure software development practices and AI-specific security risks as they pertain to a full SDLC
Strong problem-solving skills with the ability to define requirements, scope projects, and deliver solutions independently
Strong communication skills - ability to collaborate and explain complex concepts to diverse audiences, and synthesize details into actionable recommendations and implementations
Cross-functional collaboration - builds strong relationships across engineering, security, compliance, etc.

Core Competencies

Demonstrates expertise in integrating generative AI tools into software engineering workflows, enhancing productivity and software quality through AI-augmented engineering practices. Proficient in developing intelligent data processing and NLP pipelines while ensuring compliance and security in AI implementations.

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