Applied AI Engineer

Nasze wymagania:
Strong foundations in Machine Learning and modern neural network architectures.
Hands-on experience training, fine-tuning or deploying Machine Learning models.
Ability to write clean, maintainable and production-quality code.
Confidence working across multiple abstraction layers—from models and infrastructure to product behavior.
Strong problem-solving skills in ambiguous and rapidly changing environments.
A delivery-oriented mindset focused on shipping, measuring, iterating and continuously improving production systems.

O projekcie:
About the product:
Our client is developing an AI-native assistant designed to support everyday communication, organization, errands and complex workflows with minimal user input.
The product must reliably manage long-running processes, retain context, interact with external tools and complete real-world tasks despite the non-deterministic nature of modern AI models. The company’s objective is to make everyday activities significantly faster and easier for users.
About the role:
As an Applied AI Engineer, you will turn model capabilities into reliable product behavior. You will own problems end-to-end—from shaping model behavior and building the surrounding systems to ensuring that everything performs effectively in production.
The position sits at the intersection of Machine Learning, systems engineering and product development. The focus is on making AI deliver real value to users in production environments—not only in prototypes and demonstrations.
What success looks like?
• Production Machine Learning models consistently meet accuracy, latency and reliability expectations.
• Production issues are detected quickly, diagnosed effectively and resolved at the root-cause level.
• Data pipelines, training workflows and inference systems are robust, reproducible and maintainable.
• ML-powered functionality is delivered through effective collaboration with engineering, product and research teams.
• Improvements to models and systems are driven by real-world signals, measurable outcomes and user feedback.
Technology stack:
• Python
• PyTorch / JAX
• LLMs, including OpenAI-compatible APIs, LLaMA and Qwen
• Model inference and serving, including vLLM
• Vector databases

Zakres obowiązków:
Build and ship AI features end-to-end, from the model and supporting systems to the user experience.
Design and continuously improve prompts, tools, memory systems and agent workflows.
Transform raw model outputs into structured, reliable and predictable product behavior.
Diagnose and resolve issues across models, orchestration, infrastructure and user experience.
Optimize systems for latency, operational cost and production reliability.
Develop lightweight evaluation frameworks to measure real-world model and product performance.
Work closely with product and engineering teams to transform ambiguous problems into production-ready systems.