Sr. Software Engineer, AI Tooling & Enablement
Listed on 2026-05-28
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Software Development
AI Engineer, Machine Learning/ ML Engineer, Software Engineer
Sr. Software Engineer, AI Tooling & Enablement
Location: Austin, TX (hybrid)
Schedule: Full-time | Hybrid (2–3 days in office)
Work Authorization Notice:
At this time, we are unable to provide immigration sponsorship for this position. Candidates must have current, unrestricted authorization to work in the country where the role is based.
About Us
Togetherwork is a $250M recurring revenue SaaS business with more than 33 software applications serving over 12 vertical markets. We help communities, organizations, and businesses thrive by delivering purpose-built software that supports their missions and operations.
Headquartered on South Congress in Austin, TX, Togetherwork is scaling rapidly. We are customer-focused, execution-driven, and committed to operational excellence. Our teams value accountability, collaboration, and continuous improvement.
About the Role
Togetherwork is building its AI engineering practice from the ground up, and this role is at the center of it. As an AI Engineer, you will design, build, and deploy AI agents, workflows, and integrations across our software engineering and quality engineering functions. You will train our teams on how to use what you’ve built and stay connected to evolve it as the ecosystem matures.
This is not a research role. This is not a strategy role. This is a hands-on implementation role with real organizational impact from day one.
If you have a software or quality engineering foundation, have shipped AI agents and workflows into real production environments, and genuinely enjoy teaching other engineers how to work with AI — this role was written for you.
What You’ll Do
AI Agents and Workflows
- Design and implement multi-agent systems, skills, and hubs.
- Embed AI into implementation, code review, test generation, defect analysis, documentation, and developer productivity workflows across 20+ products.
Model Evaluation and Selection
- Evaluate AI model variants against real use cases and document selection decisions in a reusable framework.
- Select appropriate model tiers based on capability, cost, and latency tradeoffs.
Security and Responsible AI
- Identify and mitigate injection risks, PII exposure, and data leakage across all agent workflows.
- Embed responsible AI usage guidelines into all internal training programs.
Cost Management and Token Efficiency
- Own cost awareness across all AI integrations.
- Monitor AI infrastructure costs across products and surface optimization opportunities proactively.
Agent Versioning and Change Management
- Implement prompt versioning, regression testing frameworks, and change documentation for all AI workflows.
- Proactively manage the impact of upstream AI model updates on existing agent deployments.
Training and Enablement
- Build and deliver internal training across all engineering teams.
- Remain a long-term partner to product teams: gathering feedback, updating agents, and evolving workflows as needs change.
What You’ll Bring
- 5+ years as a software engineer, quality engineer, or architect.
- 1+ year of hands-on AI agent and workflow implementation in a real production environment.
- Direct experience with AI models (Claude Code, MCP, multi-agent orchestration, Code Rabbit, or other tools).
- Experience building and shipping multi-agent systems, skills, and hubs that real development teams depend on.
- Proven ability to evaluate and select AI models based on use case, cost, latency, and security requirements.
- Experience integrating AI into SDLC workflows: development, code review, test generation, CI/CD, or developer tooling.
- Working knowledge of AI security risks: prompt injection, PII handling, data leakage, and responsible deployment.
- Experience instrumenting AI agent workflows with observability tooling.
- Has delivered internal training to enable AI development across an engineering organization.
Preferred Qualifications
- Experience across multiple products or a platform team simultaneously.
- Familiarity with MCP servers, RAG pipelines, and vector databases.
- Background in quality engineering: test automation, coverage analysis, or defect prediction.
- Experience with prompt versioning and AI output regression testing.
Who You Are
You have a software or quality engineering…
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