From Hype to Hires, AI Engineers Rise in Poland’s Tech Scene

Meet the AI Engineer

Large Language Models (LLMs) have become widely accessible, moving AI from experiments to production. Companies now need people who can connect models to data, build services around them, and keep them fast, safe, and cost-effective. As Nvidia’s CEO has emphasized, the real risk is not losing your job to AI but to someone who uses AI well. This is where the AI Engineer comes in.

An AI Engineer is a software professional who turns LLMs and Machine Learning (ML) into real products. They bridge data science and backend engineering to ship reliable features that solve user problems at scale. It’s a relatively new role, and its scope is still being refined by employers and delivery contracts. If you’re wondering what this looks like day-to-day, here’s what they actually do:

• shipping LLM features: prompts, tools/functions, RAG¹; light tuning; backend/API² glue for speed and reliability;

• building agents: designing autonomous workflows that combine reasoning, memory, and tool use to complete complex tasks with minimal human input;

• owning data & serving: curate data, embeddings + vector search, model abstraction, pipeline orchestration, latency/cost control, defining SLO³s;

• running safely in prod: evals, guardrails (PII⁴, toxicity), monitoring/alerts, A/B tests⁵, quality/hallucination tracking, compliance. 

  <sup>1. RAG (Retrieval-Augmented Generation) is an AI approach where a model first retrieves relevant information (from a database, documents, or the web) and then generates an answer using that information.
  2. API (Application Programming Interface) is a set of rules and endpoints that let systems communicate, for example, a frontend chatbot calls an LLM API to generate answers, while the backend sends user feedback back through the API to improve results.
  3. SLO (Service Level Objective) is a specific, measurable target for the reliability or performance of a system.
  4. PII (Personally Identifiable Information) means any data that can identify a specific person.
  5. An A/B test in AI tools means showing one group the current version and another an improved version, then measuring which drives better results.</sup>

Photo Credit: CNBC

Vendor Stability Meets Stack Simplicity

As vendors stabilized LLM platforms (Azure OpenAI, AWS Bedrock, and Google Vertex AI), risk dropped and adoption accelerated. Azure OpenAI emphasizes enterprise controls and regional data protections, including the EU Data Boundary; Bedrock’s Guardrails are generally available (GA) for enforcing safety and privacy; and Vertex AI markets itself as “enterprise-ready” with security, residency, and governance. [The Official Microsoft Blog, Amazon Web Services, Inc., Google Cloud]

On the other hand, enterprises standardized on a lean stack, LangChain/LangGraph for orchestration, Databricks/MLflow or Azure ML for ML/LLMOps⁶, and vector stores like pgvector⁷ and Pinecone. [VentureBeat, MLflow, LangChain, Pincecone]. In parallel, specialized inference and gateway providers, like Together AI, Fireworks AI, OctoAI, and Groq emerged as relevant options for cost/performance-optimized serving of open models, fine-tuning workflows, and low-latency production, while fitting cleanly behind OpenAI-compatible APIs and the same orchestration/MLOps stack. [Together AI, Fireworks AI, OctoAI,  Groq]

With platform risk down and a common toolchain in place, Polish enterprises could finally move from isolated pilots to multi-product rollouts, unlocking budgets for repeatable, headcount-scalable work streams and creating clear hiring profiles, which meant bringing AI engineers on in batches, not ones and twos. Add Poland’s Microsoft-heavy estates, EU data-residency requirements⁸, and near-shoring demand from EU clients, and you get an immediate need for AI engineers who can integrate governed stacks, tune cost/latency via compatible gateways, and operate these systems reliably,  driving large-scale hiring.

<sup>6. ML/LLMOps is the practice of managing, automating, and scaling the full lifecycle of Machine Learning (ML) and Large Language Model (LLM) systems in production.
7. pgvector is an open-source PostgreSQL extension that lets you store and perform fast exact or approximate vector similarity search (e.g. embeddings) directly within PostgreSQL.
8. EU data-residency requirements mandate that personal or sensitive data about EU citizens must be stored and processed within the European Union’s geographic boundaries to comply with privacy and sovereignty laws like the GDPR.</sup>

From Trial to Rollout

Curioz.io data shows that Poland’s AI Engineer market, measured by a weighted composite of offers + signed contracts, has climbed +21.8% vs. Sep 2024 by Aug 2025. The popularity index rises steadily early, eases in spring, then re-accelerates sharply from June. Biggest monthly gains are Jan (+3.1 pp) and Jun (+5.0 pp); the last three months add +10.0 pp total (~+3.3 percentage points/month vs a long-run average of ~+2.0 pp/month).

^{Methodology and baseline. Curioz.io sets September 2024 as the baseline (index = 100). All subsequent percentage changes are measured relative to that month}

Fig 1: Increase in AI Engineer weighted composite of offers + signed contracts Sep 2024 vs. Aug 2025.

After a spring pause, momentum re-accelerated sharply in June. If current adoption and batch hiring holds, depending on a scenario:

• Conservative (trend cools to ~+1.5 pp/mo): ~+27.8%.
• Base case (last-6-month pace ~+2.0 pp/mo): ~+29.9% (~30%).
• Momentum (keep last-3-month pace ~+3.3 pp/mo): ~+35.1%.
  
  

  Assuming the summer momentum persists, Curioz.io estimates year-end growth exceeding 30% year-over-year. Contrary to seasonal norms, hiring did not decline; matured tech and the emergence of a common toolchain catalyzed a shift towards multi-product deployments, budget activations, and batch hiring for governed, reliable AI platforms.

  Batch hiring’s live. AI engineers, this is your time.

   

Fast Fills, Global Tailwinds

A key indicator in Poland’s favor is the speed at which AI engineer roles are being filled. Lightcast’s latest generative AI benchmark shows a global median posting duration of about 27 days, while Curioz.io’s composite data puts Poland’s average at roughly 23 days. For comparison, the average posting duration for IT roles more broadly is about 35.8 days in the UK and 31 days in Poland. Together, these data points highlight compressed hiring cycles for AI talent in Poland and faster role filling compared to many other tech roles.

AI adoption is moving fast. Enterprises are shifting from pilots to production in areas like customer support automation, code-assist, content generation, and analytics augmentation. Teams are increasingly platform-oriented (ML/LLMOps), which shortens iteration cycles and raises the leverage of each engineer. That, combined with competitive compensation and flexible work models, boosts offer acceptance and speeds onboarding.[Curioz.io, Lightcast]

Poland’s AI Engine, Red-Hot and Running

Poland combines tight hiring cycles (~23 days), deep hands-on talent, and a demand-rich, cloud-mature market that is rapidly industrializing AI. Curioz.io’s data shows the AI engineer market up 21.8% from September 2024 through August 2025, with an outlook to reach ~30% year over year by December. This mirrors what is visible on the ground: as platform reached maturity and common toolchains settled in, enterprises moved from trials to multi product rollouts, unlocked budgets, and began batch hiring for governed, reliable AI stacks. Crucially, a strong bench of IT professionals, especially data scientists, machine learning engineers, and analytics teams already running AI in production, is amplifying this shift by improving model quality, deployment velocity, and cross team adoption. The result is not just faster time-to-fill; it is faster time-to-value, making Poland a high throughput, low friction environment for building and scaling AI teams.

AI Engineer Is the Perfect Fit for TaaS

Why you need an AI Engineer to stay competitive

• Ship AI features faster than rivals (days → weeks, not quarters).
• Cut inference cost & latency → better margins and UX.
• Turn your data into defensible leverage (RAG/agents tied to KPIs).
• Build safe, compliant systems with evals/guardrails from day one.
• Stay vendor-flexible to avoid lock-in tax as models/tools change.

TaaS engagement pattern (30/60/90)

• 0–30 days: discovery → baseline KPIs, spike prototypes, eval harness, data/readiness audit.
• 31–60 days: harden solution, add guardrails, integrate auth/telemetry, pilot with users.
• 61–90 days: production rollout, cost/perf optimization, dashboarding, ops runbook.
  
  

Competitors are already moving.  Add a TaaS AI Engineer now to keep your edge, and compound it every sprint.

NOTE: This post is based on research by Inuits.it and Curioz.io, and has been crossposted on both platforms.