Applied Machine Learning Engineer | GenAI / LLM / ML Systems

Applied Machine Learning Engineer

GenAI / LLM / ML Systems / Production AI

Location: Remote / Hybrid
Offices: Warsaw, Kraków, Wrocław, Gdańsk
Employment type: B2B or Employment Contract
Seniority: Mid+ / Senior
Recruitment process: Remote

Salary range:
B2B: 26 000 - 42 000 PLN net + VAT
Employment Contract: 20 000 - 32 000 PLN gross

For one of our technology clients, we are looking for an Applied Machine Learning Engineer to work on modern AI systems that move beyond experiments, prototypes and isolated notebooks. This is a role for someone who wants to build AI that actually works in production: reliable, measurable, scalable and useful for real users. You will work at the intersection of Machine Learning, Generative AI, LLMs, ML systems, data pipelines and product engineering, helping build intelligent features that can be deployed, monitored, evaluated and improved over time.

About the project

Our client is developing advanced AI-powered products where Machine Learning is not an internal experiment, but a core part of the product experience. The team is building systems that use modern ML and GenAI techniques to understand data, support decision-making, automate complex workflows, generate insights, personalize user experiences and improve business processes.

Depending on your experience, you may work on recommendation systems, predictive models, classification models, NLP, LLM-based workflows, RAG pipelines, model evaluation, data processing, feature engineering, model serving or production ML infrastructure. This is a strong fit for engineers who enjoy both the modeling side and the engineering side of Machine Learning. The goal is not only to train a good model, but to make it work reliably in a real product environment.

What you will work on

• Designing, building and improving Machine Learning models for real product use cases

• Working on LLM-based applications, RAG pipelines, embeddings, semantic search or AI agents

• Developing ML pipelines for training, evaluation, deployment and monitoring

• Building features based on structured and unstructured data

• Preparing datasets, improving data quality and designing features

• Evaluating model performance using offline and online metrics

• Improving accuracy, reliability, latency, cost and stability of AI systems

• Deploying models and ML services into production environments

• Collaborating with software engineers, data engineers, product teams and business stakeholders

• Experimenting with new AI approaches and translating them into practical product features

• Building systems that can learn from feedback and improve over time
  

What we are looking for

We are looking for someone with:

• Strong experience with Python

• Practical experience in Machine Learning or Applied AI

• Experience with frameworks such as PyTorch, TensorFlow, scikit-learn or similar

• Good understanding of model training, validation, evaluation and deployment

• Experience working with real datasets and production-oriented ML problems

• Ability to write clean, maintainable and testable code

• Good understanding of data processing, feature engineering and model performance metrics

• Experience with APIs, backend services or ML model serving

• Ability to work closely with product and engineering teams

• Good problem-solving skills and ownership mindset

• English allowing you to work in an international technical environment
  

Nice to have

It would be great if you also have experience with:

• LLMs, GenAI or NLP systems

• RAG, vector databases, embeddings or semantic search

• LangChain, LangGraph, LlamaIndex, OpenAI API, Anthropic API or similar tools

• Fine-tuning, prompt engineering, model evaluation or guardrails

• MLOps tools such as MLflow, Weights & Biases, Airflow, Kubeflow or similar

• Cloud platforms such as AWS, GCP or Azure

• Docker, Kubernetes or CI/CD

• Model serving with FastAPI, BentoML, TorchServe, Triton or similar

• Data warehouses, data lakes or modern data platforms

• Recommendation systems, ranking models, forecasting, classification or anomaly detection

• A/B testing, experimentation or production monitoring
  

Tech stack

Core:
Python, Machine Learning, PyTorch/TensorFlow/scikit-learn, SQL, APIs, data processing

Nice to have:
LLMs, RAG, embeddings, vector databases, LangChain/LangGraph, MLflow, Docker, Kubernetes, AWS/GCP/Azure, FastAPI

What the client offers

• Work on modern AI and Machine Learning products

• Opportunity to build production-grade AI systems, not only experiments

• Projects involving GenAI, LLMs, ML systems, data and product intelligence

• Strong technical team and space for ownership

• Flexible work model: fully remote or hybrid

• Offices in Warsaw, Kraków, Wrocław and Gdańsk

• Fast and transparent recruitment process

• B2B or Employment Contract

• Attractive salary depending on experience

• Opportunity to grow in one of the fastest-growing areas of engineering