Description:
I’m helping Rozeta Labs find a top candidate to join their team full-time for the role of Applied AI Engineer- Agentic Systems.
You'll transform messy business processes into AI-enabled systems, delivering real-world operational impact.
Compensation:
USD 4K - 6K/month
+ Bonuses (up to 50% of base compensation)
Location:
Remote (anywhere)
Mission of Rozeta Labs:
"To help companies scale faster by integrating AI agents into real operational workflows."
What makes you a strong candidate:
Responsibilities and more:
About Rozeta Labs:
- Rozeta Labs helps mid-market operating companies turn messy business processes into AI-enabled systems that actually work in production.
- We embed with subject matter experts, map how work actually gets done, identify the constraints, and build systems that help companies move work from request to resolution.
- That means working across CRMs, ERPs, inboxes, spreadsheets, internal docs, permissions, approvals, edge cases, and the human beings who keep the business running.
- We are looking for an Applied AI Engineer who can help us design, build, and deploy agentic workflows for real operating companies.
The Role:
- This is a builder role for someone who wants to work at the edge of applied AI.
- You will work directly with operators, business owners, and internal subject matter experts to understand how a company actually runs, then turn that understanding into working AI systems.
- That might mean building an agent that reviews inbound requests, pulls context from multiple systems, drafts the right response, routes approvals, updates the CRM, creates follow-up tasks, and escalates exceptions to the right human.
- It might also mean building the boring but critical pieces around the agent: data pipelines, tool integrations, retrieval systems, evaluation loops, human review queues, audit trails, permissions, and monitoring.
What You’ll Do:
- You will design and build AI agents, internal tools, workflow engines, automations, and integrations for mid-market companies.
- You will work with client teams to understand real business processes, not sanitized diagrams.
- You will connect AI systems to the tools companies already use: CRMs, ERPs, email, Slack, Google Workspace, Microsoft 365, databases, spreadsheets, ticketing systems, and vertical SaaS platforms.
- You will build prototypes quickly, test them with real users, and turn the useful ones into production-grade systems.
- You will design human-in-the-loop workflows where AI drafts, recommends, routes, summarizes, researches, classifies, or executes — but humans remain in control where judgment matters.
- You will create systems that handle edge cases, bad data, missing context, permission issues, unclear ownership, and the weird “this is just how we do it” logic that exists inside every company.
- You will help define technical patterns for Rozeta’s implementation work so we can move faster across clients without shipping fragile one-off hacks.
What We’re Looking For:
- You are a strong engineer who is excited by applied AI, not just model benchmarks.
- You can build useful software quickly.
- You understand LLMs, agents, retrieval, tool use, structured outputs, evals, and orchestration- but you also understand that the hard part is usually the business process, not the model call.
- You are comfortable working in ambiguous environments where the requirements are messy at first.
- You can talk to non-technical operators, translate what they mean, and build what they actually need.
- You care about shipping systems that survive contact with the real world.
Useful Experience:
- Strong experience with TypeScript, Python, or both.
- Experience building with LLM APIs such as OpenAI, Anthropic, Gemini, or similar.
- Experience with AI agent frameworks, workflow orchestration, structured outputs, retrieval, vector databases, tool calling, and evals.
- Experience integrating with third-party APIs, webhooks, databases, CRMs, internal tools, and SaaS platforms.
- Experience building internal tools, dashboards, admin panels, approval flows, or human review queues.
- Experience with cloud platforms such as Cloudflare, AWS, GCP, Azure, Supabase, or Vercel.
- Experience working with messy business systems is a major plus.
- Experience in home services, healthcare, insurance, logistics, field services, finance ops, revenue ops, or other operationally complex industries is a bonus.
You’ll Be a Great Fit If:
- You like turning vague business pain into working systems.
- You enjoy talking to operators and figuring out what is actually broken.
- You are not precious about tools, frameworks, or hype cycles.
- You can move fast without being reckless.
- You are comfortable building the first version, watching it break, learning why, and making it better.
- You understand that production AI requires guardrails, evals, logging, observability, permissioning, and human escalation.
- You care more about business outcomes than clever architecture.
- You would rather ship something useful than debate whether it counts as “agentic.”
You May Not Be a Fit If:
- You only want to work on model research.
- You mainly want to build demos.
- You think a chatbot is the answer to every workflow problem.
- You need perfectly written requirements before you can start.
- You are uncomfortable talking directly to clients or operators.
Example Projects:
- Build an AI intake agent that reads inbound customer requests, classifies the issue, pulls account context, drafts a response, creates a task, and routes exceptions to a manager.
- Build a finance ops agent that reconciles invoices, flags mismatches, drafts vendor follow-ups, and prepares approval packets.
- Build a revenue ops agent that reviews CRM hygiene, identifies stalled deals, summarizes account history, and recommends next actions.
- Build a field operations assistant that turns job notes, photos, forms, and customer messages into structured records and follow-up tasks.
- Build a knowledge agent that helps employees find the right SOP, policy, customer context, or internal precedent without searching across ten systems.
- Build an executive reporting agent that pulls data from multiple systems, explains what changed, and highlights where leadership should focus.
How We Work:
- We move fast.
- We get close to the business.
- We ask annoying questions until the real constraint is obvious.
- We prototype quickly, ship carefully, and improve based on how people actually use the system.
- We believe AI implementation is not about replacing teams.
- It is about giving teams better systems, better leverage, and less manual drag.
Compensation:
- Competitive compensation based on experience.
- Open to full-time, contract-to-hire, or exceptional fractional candidates.
We care less about credentials and more about evidence that you can ship useful AI systems in the real world.
Your potential leader(s):