AI agent and LLM engineers who build for production, not demos.
Building an agent that works once is easy; building one you can trust is engineering. Our AI engineers design tool-using and multi-agent systems, RAG assistants and MCP integrations, with evaluation suites, guardrails and human review built in from the start.
- Matched to your stack and domain
- NDA and IP terms agreed upfront
- CMMI / ITIL-aligned delivery
- Individual, dedicated team or project-based
What our ai agent & llm engineers work on
- Agent design: tools, permissions, planning and memory
- RAG pipelines: chunking, metadata, retrieval and re-ranking
- Evaluation sets and regression testing for prompts, tools and models
- Guardrails, approval steps and audit logging
- Integration with enterprise systems through APIs and MCP servers
When it makes sense to add this role
- You want to automate a document- or text-heavy workflow
- A prototype agent needs to be made reliable and auditable
- You need a knowledge assistant over internal documents
- Your team wants to learn agent engineering alongside experienced practitioners
Skills and tools
How to engage ai agent & llm engineers through RFsoftLab
- 1
Share requirements
Tell us the role, stack, seniority and start date. A short call or the form below is enough.
- 2
Receive matched profiles
We propose people whose experience fits your stack, domain and governance needs.
- 3
Interview and choose
Run your own technical or domain conversation. Nobody starts without your approval.
- 4
Onboard and deliver
Access, tooling and reporting are set up and the engagement begins on the agreed date.
Senior architects review each requirement and propose placements on technical fit, domain and governance fit, communication and confidentiality. Delivery can be coordinated by a project manager from our team, and deliverables and IP are assigned to you under contract. Pricing follows project scope, duration and the seniority required. See the AI Agent Development & Automation service for project-based work, or read about our full staff augmentation models.
Start a conversation
Tell us what you need. We reply within one business day.
Share a few details and a senior member of our team will come back with questions, matched profiles or a proposed approach. NDA available on request.
- Reply within one business day
- NDA signed before you share details
- Speak directly with a senior architect or engineer
Prefer to share a detailed brief? Use the full project form or explore the talent catalogue.
Questions
Straight answers before we start.
How we work, what we staff and how engagements are structured. For anything else, write to us.
Read before you engage
- AI Agents in Enterprise Workflows: A Production Checklist
A demo agent and a production agent are different products. This checklist covers scope, permissions, tools, evaluation, observability and ownership before an agent touches real enterprise systems.
- Guardrails and Evals: How to Test an AI Agent Before You Trust It
Guardrails limit what an agent can do; evaluations tell you whether it does the right thing. You need both, and you need them before launch, not after the first incident.
- RAG vs Fine-Tuning for Regulated Enterprise Data
Retrieval-augmented generation and fine-tuning solve different problems. For regulated, frequently changing knowledge, the choice has consequences for auditability, access control and cost of change.