Middle Python Developer (ML)
Warsaw, Masovian Voivodeship, Poland
- Pay
- Pay not listed
- Work setting
Remote — work setting source
Work setup in the employer’s posting: Remote
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: Full-time
From the employer’s posting
What you’ll work on
Full postingDesign and implement LLM-based features and RAG pipelines
Build and optimize retrieval using vector embeddings and hybrid search
Clean and structure source data to improve AI output relevance
Evaluate model quality and bring proven solutions into production
Own changes end-to-end, from design through deployment
From the employer’s posting
Design and implement LLM-based features and RAG pipelines from data ingestion to answer generation
Build and optimize retrieval using vector embeddings and hybrid semantic and keyword search
Process, clean, and structure source information to improve the accuracy and relevance of AI-generated output
Evaluate model quality, experiment with new approaches, and bring proven solutions into production
Own changes end-to-end, including design, implementation, testing, and deployment
What you’ll bring
All qualificationsCore experience
- 3+ years of commercial software development experience
- Experience with knowledge graphs or graph databases
- 2+ years of experience working on AI/ML projects
- Good Python skills with hands-on implementation experience
- Practical experience with LLMs and modern AI technologies
- Practical experience with RAG architecture, vector embeddings, and hybrid search
Qualification wording
3+ years of commercial software development experience
Experience with knowledge graphs or graph databases
2+ years of experience working on AI/ML projects
Good Python skills with hands-on implementation experience
Practical experience with LLMs and modern AI technologies
Practical experience with RAG architecture, vector embeddings, and hybrid search
Tools in this posting
- Python
Source — Tool mentions in context
- 2+ years of experience working on AI/ML projects - Good Python skills with hands-on implementation experience - Practical experience with LLMs and modern AI technologies
You will join one of two AI initiatives: ChatCPI, an LLM-powered assistant for intelligent search and question answering over large technical documentation, or a Knowledge Graph Platform focused on structuring and connecting enterprise information for AI-driven use cases. Technologies: Python, LLMs, RAG, vector embeddings, hybrid search, knowledge graphs.
Posting record
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- Live
- First seen by us
- Sep 29, 2026 · 30 days ago
- Recorded sightings
- 97
- Last seen by us
- Oct 29, 2026
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Job description
Job Description
- Design and implement LLM-based features and RAG pipelines from data ingestion to answer generation
- Build and optimize retrieval using vector embeddings and hybrid semantic and keyword search
- Process, clean, and structure source information to improve the accuracy and relevance of AI-generated output
- Evaluate model quality, experiment with new approaches, and bring proven solutions into production
- Work closely with the team to integrate AI components into the wider platform
- Own changes end-to-end, including design, implementation, testing, and deployment
- Work independently while proactively communicating blockers, scope questions, and technical trade-offs
Qualifications
- 3+ years of commercial software development experience
- 2+ years of experience working on AI/ML projects
- Good Python skills with hands-on implementation experience
- Practical experience with LLMs and modern AI technologies
- Practical experience with RAG architecture, vector embeddings, and hybrid search
- Experience with information processing aimed at improving AI quality, including data preparation, chunking, metadata enrichment, and relevance tuning
- Ability to work independently and take ownership of tasks from design through deployment
- Upper-Intermediate English
WILL BE A PLUS
- Experience with knowledge graphs or graph databases
- Background in the telecommunications domain
Company Description
You will join one of two AI initiatives: ChatCPI, an LLM-powered assistant for intelligent search and question answering over large technical documentation, or a Knowledge Graph Platform focused on structuring and connecting enterprise information for AI-driven use cases.
CUSTOMER
Our Customer is a leading Swedish telecommunications company recognized for delivering innovative large-scale technology solutions across complex infrastructure environments. The company operates internationally, serving millions of users and working with global technology leaders to drive digital transformation in the telecommunications industry.
PROJECT
You will join one of two AI initiatives: ChatCPI, an LLM-powered assistant for intelligent search and question answering over large technical documentation, or a Knowledge Graph Platform focused on structuring and connecting enterprise information for AI-driven use cases.
Technologies: Python, LLMs, RAG, vector embeddings, hybrid search, knowledge graphs.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Warsaw, Masovian Voivodeship, Poland
- Design and implement LLM-based features and RAG pipelines from data ingestion to answer generation - Build and optimize retrieval using vector embeddings and hybrid semantic and keyword search - Process, clean, and structure source information to improve the accuracy and relevance of AI-generated output
More source context
- Practical experience with LLMs and modern AI technologies - Practical experience with RAG architecture, vector embeddings, and hybrid search - Experience with information processing aimed at improving AI quality, including data preparation, chunking, metadata enrichment, and relevance tuning
More relevant text appears in the full description.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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