Applied Machine Learning Engineer | GenAI / LLM / ML Systems
Yard Corporate
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