Staff augmentation
Senior engineers who join your stand-ups and work in your tools.
Best when
You have a roadmap and need more capacity or a specific skill.
Hire senior AI engineers
Engineers who take language-model features from a demo to production: retrieval over your own data, agents that call your APIs safely, and evaluation that tells you whether a change made answers better or worse.
Short answer
elitics provides senior AI engineers from Prishtina who build LLM features, retrieval-augmented search and agents in your product, measure answer quality with evaluation sets, and keep cost and latency under control. You interview the engineers, they can start about 48 hours after you approve their profiles, and team extension costs €45–65 an hour with a two-week trial.
- per engineer-hour, published on our pricing page
- €45–65
- from approving profiles to the first commit
- ~48 h
- retrieval, reranking and citations over your data
- RAG
- answer quality tested in CI before release
- Evals
What our AI engineers build
- Search and answers over your documents
- Retrieval-augmented generation over contracts, manuals, tickets or a knowledge base, with source citations so users can check every answer.
- AI features inside an existing product
- Summaries, classification, data extraction from PDFs and emails, and drafting tools added to the SaaS product you already run.
- Agents that use your tools
- Assistants that call your APIs to look up orders, create tickets or update records, with permissions and confirmation steps for anything that changes data.
- Evaluation and monitoring
- Golden question sets, automated scoring and tracing in production, so prompt or model changes are measured instead of guessed.
- Classical machine learning
- Forecasting, scoring and anomaly detection with TensorFlow and the Python data stack when a trained model fits better than a language model.
What a senior AI engineer should own
Calling a model API is the easy part. These are the parts that decide whether the feature survives contact with real users.
- Retrieval quality
- Chunking, hybrid keyword and vector search, reranking and metadata filters, measured separately from the quality of the generated answer.
- Structured, validated output
- JSON output checked against a schema, retries on invalid responses, and no free text passed straight into your database or UI.
- Cost and latency
- Caching, routing simple requests to smaller models, trimming context and streaming responses, with a cost per request that is known and tracked.
- Safety and data handling
- Prompt-injection defences for content from users and documents, redaction of personal data, and provider settings that keep your data out of model training.
Questions to ask an AI engineer
Use these in your own interview, with us or with anyone else. A strong candidate answers with specifics from systems they have run.
- 01
How would you know if our RAG answers are correct?
Look for a test set of real questions with expected sources, separate scores for retrieval and for answer faithfulness, and a regression run before each release.
- 02
When would you fine-tune instead of using retrieval?
Retrieval for knowledge that changes or needs citations; fine-tuning for output format, tone or latency on a narrow task. A good answer mentions the cost of keeping a fine-tuned model current.
- 03
How do you stop an agent from doing damage?
An allow-list of tools, the user’s own permissions on every call, confirmation before anything irreversible, step and spend limits, and a full log of actions.
- 04
How would you halve our model bill?
Measure cost per feature first, then cache repeated prompts, route easy cases to a cheaper model, shorten the context and batch offline work.
- 05
What data leaves our systems, and where does it go?
The candidate should name the provider’s retention and training terms, the region, what is redacted before sending, and what is logged on your side.
How hiring an engineer works
- 01
Tell us the role
Stack, seniority, start date and the hours you need to share. A call with our engineering lead takes 30 minutes.
- 02
Review profiles
You receive engineer profiles matched to your stack and interview the ones you choose.
- 03
Start in about 48 hours
NDA and IP assignment are signed, and the engineers are set up in your repositories and tools.
- 04
Two-week trial
If you are not satisfied, you do not pay for those two weeks. An engineer who is not the right fit is replaced at no extra cost.
How clients usually start
- One AI engineer in your product team
- Joins your stand-ups and repositories to ship a first AI feature alongside your own developers.
- AI pair
- An AI engineer with a full-stack engineer, so the feature ships with its UI, API and monitoring rather than as a prototype.
- Fixed-price pilot
- A scoped proof of value with an agreed evaluation set and a go or no-go decision at the end.
Ways to work with us
Dedicated squad
A cross-functional team (product, engineering, QA, DevOps) that owns a product or feature.
Best when
You want a product built or rebuilt without growing headcount.
Build-Operate-Transfer
Your own development center in Kosovo, set up and run by us.
Best when
You plan a long-term engineering presence in Europe.
Prefer a delivered project? AI & Machine Learning Development Company
Common questions
Which models and tools do your AI engineers use?
Most often Gemini and the OpenAI API, LangChain for orchestration, Pinecone or Weaviate for vector search, and TensorFlow with Pandas and NumPy for machine learning. We work with the provider you have already approved.
Can the engineers work only inside our cloud?
Yes. They work in your repositories and cloud accounts under your access policies, and production data stays in your environment.
Is an AI engineer the same as a data scientist?
Not quite. Our AI engineers are software engineers who ship and run AI features in production. For forecasting or scoring models they use classical machine learning, but the focus is a working product, not a notebook.
What does a senior AI engineer cost?
Team extension costs €45–65 per engineer-hour, depending on seniority. New engagements start with a two-week trial: if you are not satisfied, you do not pay for those two weeks.
Related
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- LLM Integration & Custom AI App DevelopmentTurn GPT, Claude or Gemini into a product: AI apps, assistants and LLM integrations built on your data and workflows. Senior team in Kosovo, MVP in weeks.
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Let’s build what comes next.
Tell us what you need to build or scale. You will get a proposal and estimate within 24 hours.