We’re expanding our AI engineering team and looking for junior Machine Learning / AI Engineers who want to grow into building real production AI features across document generation, user and organization memory, intelligent navigation, recommendation systems, and agentic workflows.

This role is a good fit for someone who may not have much formal professional experience yet, but has strong motivation, good fundamentals, and a serious interest in machine learning, AI engineering, mathematics, statistics, Python, and production software systems.

We don’t expect you to already know everything. What matters most is that you are curious, reliable, willing to learn, and able to think clearly about technical problems.

We are open to two junior profiles: one more oriented toward AI engineering, Python/backend, LLM systems, agents, document generation and production AI features; and another more oriented toward ML/RL/recommendation, ranking, personalization, optimization, feedback loops and statistical modeling.

You don’t need to fit both profiles perfectly. If you have strong fundamentals, work ethic, curiosity, and willingness to learn, the rest can be learned through real work.

Sounds interesting? Find out more!

What you’ll be doing:

Build and improve AI/ML features in Python, including APIs, workers, async pipelines, storage integrations, and service workflows.
Work on real product features such as document generation, template editing, memory retrieval, intelligent navigation, recommendation systems, evaluation pipelines, and agentic workflows.
Apply ML, statistics, evaluation methods and feedback-loop thinking to ranking, recommendations, personalization, prediction, and output-quality measurement.
Learn how production AI/ML systems are designed, built, evaluated, deployed, monitored, and improved over time.
Use LLMs, embeddings, classifiers, rerankers, structured outputs, RAG, smaller open-source models, and AI-assisted engineering tools where useful.
Write clean, understandable and maintainable code, while developing good engineering habits around testing, debugging, logging, code reviews and deployment readiness.

What we are looking for in a Junior Machine Learning Engineer / AI Engineer is:

Strong motivation to learn and work seriously in AI/ML engineering.
Good fundamentals in Python, software engineering, Git, debugging, testing, APIs, databases, queues, or data pipelines.
Good foundations in mathematics, statistics, probability, linear algebra, optimization, and classical ML concepts such as classification, regression, clustering, ranking, feature engineering and model evaluation.
Familiarity with tools such as pandas, NumPy, scikit-learn, or similar.
Interest in LLMs, embeddings, RAG, structured outputs, tool calling, agentic workflows, recommendation systems, personalization, reinforcement learning, ranking or feedback loops.
Ability to reason about noisy data, biased signals, metrics, evaluation tradeoffs, and real product behaviour.
Willingness to ask questions, learn fast, take feedback well, and take ownership step by step.
Professional working proficiency in English.

It would be cool if you also have, but it’s not necessary:

Personal, university, internship, research, or open-source projects in ML, AI, backend, data, or Python.
Exposure to reinforcement learning, contextual bandits, recommendation systems, personalization, search relevance, reward functions, or feedback-loop-based learning.
Exposure to LLMs, embeddings, RAG, deep learning, open-source models, structured outputs, tool calling, model evaluation, or agentic frameworks such as OpenAI Agents SDK or similar.
Basic experience with Docker, cloud infrastructure, CI/CD, GitHub Actions, Terraform, Kubernetes, Azure, observability, monitoring or production deployment workflows.
Strong mathematical curiosity or interest in advanced methods such as conformal prediction, uncertainty quantification, topological data analysis, causal inference, or optimization.
Interest in memory-aware agents, document generation systems, workflow automation, intelligent SaaS products, and building practical AI systems that users actually rely on.

A little bit about us:

Pontis Technology is a Croatian IT services company with offices in Croatia, Germany, and Bosnia and Herzegovina. In 2025, we merged with our sister company, Provoke Solutions, becoming a Ponteva company headquartered in Dallas. Ponteva is a member of the Bridgewest Group, a global private investment firm.

We design and deliver advanced software and AI-driven solutions for global companies, helping them innovate, scale, and transform through technology. Our full-cycle development capabilities are powered by teams of Front-end and Back-end Engineers, Machine Learning Engineers, DevOps Engineers, Designers, QA Engineers, Data Engineers, Project Managers, and Business Analysts.

In 2024, we made our first acquisition, successfully acquiring Clover Studio — a company with international clients specializing in app development and design — and established our Machine Learning team of 20 experts, further expanding our capabilities in AI and intelligent automation.

Today, Pontis Technology brings together more than 130 professionals who combine engineering expertise, creativity, and collaboration to deliver cutting-edge solutions for international clients. This commitment to quality and collaboration has earned us recognition from Alma Career's (Moj Posao) independent survey as Croatia’s Best New Company based on employee feedback.

If you’re as passionate about technology and innovation as we are — or simply curious to learn more about us and our projects — send us your application!

We can’t wait to welcome new members to our team.

Here’s a quick look at the benefits and perks we offer to our employees:

Junior Machine Learning Engineer / AI Engineer (m/f)