>_ Tibor Sloboda

AI consultant & research engineer · slovakia

Tibor SlobodaAI research · systems engineering

I build AI software, data pipelines, and the infrastructure they run on. I can take on the implementation, lead your engineers, or help you work out what is worth building.

fig. 0 · gradient descent with momentum on a synthetic loss surface ℒ(θ). Convergence after 263 steps.

My work combines AI research with production engineering. I have led AI teams, co-developed data and LLM systems, and applied machine learning to problems in medicine and chemistry. The work has to fit the available data, hardware, and budget, and be worth doing in the first place.

§1Start with your problem

See pricing ->
01 We know what we need. We need someone to build it.You have requirements, a specification, or a roadmap. I can build the software, lead your engineers, or do both. 02 Would AI actually help here?You have an idea, some data, or a business problem. I assess what is feasible, what it would cost, and whether AI is the right tool. 03 It works, but it needs to work better.Slow inference, unreliable results, rising cloud bills, or failures between components. I investigate the cause and implement the fix. 04 We need something unusual built or investigated.You need to implement a research result, investigate an unfamiliar domain, or work around a model or hardware constraint. I enjoy this kind of work. 05 Our team needs to understand and use this.Practical training shaped around your team and its work, including AI fundamentals, LLMs for software engineering, and safe use of AI tools.

Flexible rates, excluding applicable VAT. Free 15-minute introductory call.

§2Explore the site

fig. 1 · computed from this site's text
Corpus map of this site Every frequent term from the site text, embedded in 2-d from PPMI co-occurrence statistics and grouped into 5 clusters. 5 capability terms are highlighted as links. work research systems span data model class div production figure software hardware index models system problem where slovak services deployment engineering leadership evaluation figcaption nbsp focus-list strong technical includes start rates boundary logs data-area whether needs help learning infrastructure june policy actual operating threshold mdash medicine chemistry how including call discovery machine implementation selected information university technology focus fiit different results tools latency architecture tool llm worked constraints teams building prediction people why tell time consulting phd runtime intended blog path hours possible slovakia contact engineer available present Local Inference AI Guardrails Rust Infrastructure Research Production MLOps
fig. 1 · words grouped by how often they occur together in the site's text, using PPMI weighting, spectral projection, and five clusters. Ringed nodes link to technical topics.

§3§2Areas of expertise

fig. 21 · experience profile

Machine learning and multidisciplinary research are where I have the most experience. The scores below are my own assessment.

Areas of expertise Self-assessed depth of experience. Machine learning: 9/10; Data engineering: 8/10; AI infrastructure: 6.5/10; Software engineering: 8/10; Security: 6.5/10; Multidisciplinary research: 10/10; Machinelearning Dataengineering AIinfrastructure Softwareengineering Security Multidisciplinaryresearch 0 2 4 6 8 10 9 8 6.5 8 6.5 10
Machine learning9/10
Data engineering8/10
AI infrastructure6.5/10
Software engineering8/10
Security6.5/10
Multidisciplinary research10/10
fig. 21 · self-assessed depth of experience, on a scale from 0 to 10.

§4§3Selected work

full portfolio ->

I led the team and co-developed an automated pipeline that turns unstructured, inconsistent inputs into standardized business data.

§5§4Research & writing

Published research, software experiments, and opinions I have spent too long thinking about.

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