Accountable AI staffing solutions
Add ML engineers, data scientists, data engineers or labeling specialists to the team you already run. They take direction from your leads, we stay their employer, and most start within 5–10 working days.
What AI staffing means at Dignep
AI staffing is staff augmentation for AI work. You add one to five specialists to your own team, for as long as you need them, and manage their work day to day. We handle sourcing, technical screening, contracts and payroll in Nepal.
If you would rather hand us the outcome than manage the people, look at AI/ML engineering or AI MVP development instead.
AI roles you can staff with Dignep
These are the roles clients add most often. Each engineer is screened on the skills listed before you see a CV.
Machine learning engineers
Design, train and evaluate models for NLP, computer vision, recommendation and LLM-based systems. Typical skills: Python, PyTorch or TensorFlow, LangChain, vector databases, model evaluation, MLOps.
Data scientists
Turn data into decisions through analysis, experiments and forecasting. Typical skills: statistical modelling, experiment design, BI tools, dashboards and explaining results to non-specialists.
Data engineers
Build the pipelines and storage that AI and analytics depend on. Typical skills: ETL and ELT pipelines, data warehousing, streaming, SQL, Python, dbt, Airflow and cloud data platforms.
Software engineers for AI applications
Ship the product around the model: APIs, interfaces and integrations. Typical skills: Node.js, React, TypeScript, REST and GraphQL, LLM API integration, security and performance work.
Data labeling specialists
Human-in-the-loop annotation for text, images, audio and video, plus the guidelines and QA that keep labels consistent across training and evaluation sets.
Why staff AI roles through Dignep
What you get beyond a CV, and what stays our job once the specialist joins your team.
An accountable employer
We stay the employer of record, with contracts, payroll and HR handled in Nepal and documented on our side.
Experience where scrutiny is high
Public-sector and donor-funded systems, a SOC platform and a GRC product: work where reliability, documentation and governance get checked.
Room to grow
Start with one specialist and add up to five. When the work outgrows that, move to a dedicated AI team of three to fifteen or more engineers.
Honest working hours
We share most of the day with India, part of the day with Australia, the UK and Europe, and agree a fixed call window with US clients.
Exit terms in writing
Thirty days' written notice to scale down or stop, stated in the proposal before anyone starts.
AI staffing: frequently asked questions
What is AI staff augmentation?
Extending your own team with remote AI specialists employed by Dignep in Nepal: ML engineers, data scientists, data engineers, AI application developers and data labelers. They work as part of your product team for as long as you need them.
How quickly can a remote ML engineer start?
Typically 5–10 working days from your request, including your interviews. A whole dedicated AI team takes two to four weeks.
How is AI staffing priced?
By the seniority of each specialist and how long you need them. We quote after a free 30-minute call and send a written proposal within two working days.
Which time zones can your AI engineers cover?
We work 09:00–18:00 Nepal Time (UTC+5:45): almost the whole Indian day, about 3–4 hours of the Sydney afternoon, the European morning and about 3 hours of the UK day. For the US we agree a fixed daily call window before kickoff.
Which AI roles do you actually need?
Usually fewer than you think, and not always the one in the job ad. Most AI projects stall on data or on getting a model into production, not on the model itself. Start with the gap that blocks you today, then add people as the work proves itself.
| Your situation | Add first |
|---|---|
| You want an LLM feature, retrieval (RAG) or an agent inside your product | An ML engineer with LLM experience, plus a software engineer to build the product around it |
| The model works in a notebook but not reliably in production | An ML engineer with MLOps experience. See also MLOps and AI infrastructure |
| Your data is scattered, late or dirty, and models keep breaking on it | A data engineer. See data engineering |
| You need to know what the data says before you build anything | A data scientist |
| You need labelled training data or human evaluation of model outputs | Data labeling specialists. See data labeling services |
Request to first day in 5–10 working days
You interview everyone before they start. We don't skip that step to hit a date. Once they join, specialists work under our ISO/IEC 20000-1 certified service management.
Pricing is scoped and quoted after a free 30-minute discovery call, with a written proposal within two working days.
Discovery call
Thirty minutes, free. The problem, the roles you think you need and the ones we think you need, your stack and your working hours.
Written proposal (within 2 working days)
Roles, seniority, pricing and start dates.
Shortlist and interviews
Screened profiles with sample work. You interview, and you can set a short task on your own codebase.
First day (within 5–10 working days of your request)
Access to your repositories, data and tools under the controls you set. We sign your NDA and data-handling terms first.
Review and adjust
Regular check-ins. Add specialists as the work grows; give 30 days' written notice to scale down.
Your data stays under your controls
Engineers work in your environments, with the access you grant and remove. NDAs and data-handling terms are signed before access is given.
Governance when you need it
For regulated AI work, our governance team covers ISO 27001, SOC 2, the EU AI Act, NIST AI RMF and Nepal Rastra Bank AI guidance. See AI governance and security.
Hours stated up front
09:00–18:00 Nepal Time. For US teams, the daily call window is written into the proposal, not discovered in week two.
Staffing, a dedicated AI team, or a managed build?
| Option | Size | Start time | Who runs the work |
|---|---|---|---|
| AI staffing (this page) | 1–5 specialists | 5–10 working days | You, day to day |
| Dedicated AI team | 3–15+ engineers | 2–4 weeks | You set priorities; the team has its own lead |
| AI MVP or PoC | Small squad | 4–8 week build | We do, against agreed success criteria |
Pricing is scoped and quoted after a free 30-minute discovery call, with a written proposal within two working days. Staffing and dedicated teams run on 30 days' written notice.
When this isn't the right fit
- Nobody on your side can direct AI work or review it. Augmented specialists need a lead; a managed AI engagement gives you one.
- You need one person for a two-week spike. Onboarding takes part of that time; a short scoped project is better value.
- You need someone on site. Our engineers work remotely from Nepal.
- You want a research partner for open-ended work with no product behind it. Talk to us about DAIR instead.
DAIR, the Dignep AI Research Unit
Our AI engineers work with our applied research unit:
DAIR is our applied AI research unit, led by Dr. Yagya Raj Pandeya of Kathmandu University's Department of Artificial Intelligence. Its work covers low-resource NLP for Nepali, retrieval-augmented generation, and safety tooling such as hallucination detection and red-teaming.
Further reading
Need AI engineers on your team?
Book a free 30-minute call. You get a written proposal within two working days.
