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A technology firm in Poland is looking for an AI Engineer to develop LLM-powered products with a focus on natural language processing. You will take part in a greenfield project, ensuring high quality and performance in user-facing systems. The ideal candidate has solid experience in production NLP or LLM systems with a strong Python background. Flexible remote work and a comprehensive benefits package are provided, alongside the opportunity to impact a growing product significantly.
Location: Poland
Contract: B2B or contract of employment
We’re building an intelligent AI shopping assistant in partnership with one of the fastest-growing companies in Poland (InPost) – a system that truly understands natural language and conversational context. We’re early in the journey, which means real influence on architecture, models, and the final shape of the product.
You will start by building the system as part of our team, working in a focused, greenfield setup. After a few months of introduction to the project, you will seamlessly continue working on the same product directly with the client, becoming part of the InPosts team responsible for its long-term development and scaling. There is no handover phase and no context switching - you stay with the same codebase, product, and technical challenges, while gaining long-term ownership and impact.
Designing, implementing, and deploying end-to-end NLP and deep learning systems
Building LLM-powered applications that interact with real users
Developing and maintaining production Python services
Exposing models and pipelines via REST APIs (FastAPI, Flask)
Working on retrieval models and techniques (RAG, embeddings, ranking)
Evaluating, monitoring, and continuously improving model and system quality
Scaling systems to handle enormous volumes of requests
Greenfield project built from scratch
High-scale, user-facing systems with strict performance and reliability requirements
Designing systems meant for long-term ownership, not short-term delivery
Balancing model quality, latency, and cost in production LLM systems
How to build LLM-powered products from scratch and take them to production
Proven approaches to running LLMs in production at scale
How to design, evaluate, and evolve NLP systems used by real users
Best practices for production ML and AI system architecture
End-to-end ownership of LLM-based systems
Optimizing retrieval models, RAG pipelines, and inference workflows
Experimenting with different LLMs, prompting strategies, and system designs
Solving performance and reliability challenges under heavy traffic
Proven experience designing and deploying end-to-end NLP and deep learning solutions in production environments
Hands-on experience building LLM-powered production systems (e.g. GPT, Claude, Gemini), including prompt engineering, evaluation, fine-tuning, and user-facing integrations
Python proficiency with experience building and maintaining reliable production services and data pipelines
Strong software engineering mindset, including code quality, testing, scalability, and production deployments
Experience building RESTful APIs (FastAPI, Flask) to expose ML/LLM capabilities
Curiosity and commitment to continuous learning in the NLP/LLM/AI space
Collaborative team-player with strong communication skills
PyTorch, Hugging Face, and modern ML tooling for training and inference
MLOps practices and tooling
RAG systems, vector databases, and retrieval optimization
high-traffic, high-availability systems
At least 1 year of experience working with NLP / LLM systems in production
Experience contributing to production ML or AI services
Eagerness to learn and grow in a fast-moving environment
At least 4 years of experience in ML / AI engineering
Proven experience owning production NLP or LLM systems
Strong understanding of scalability, performance, and system design
At least 6 years of experience in ML / AI engineering
Experience designing large-scale, production LLM architectures
Ability to drive technical direction and mentor other engineers