Senior Python/AI Engineer
- Compensation
- Not disclosed
- Location
- Lviv, UA
- Work setup
- Hybrid
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 25+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- Strong hands-on Python backend development experience, using frameworks such as FastAPI or similar.
- Proven experience designing and developing APIs and integrating asynchronous or external services.
- Hands-on experience building and improving AI/GenAI solutions, particularly retrieval/RAG, document understanding or knowledge-intensive assistants.
- Strong understanding of information retrieval, embeddings, vector search and hybrid search.
- Practical experience evaluating AI solutions using benchmark or golden datasets, regression testing, error analysis, evaluation metrics or judges, and human/SME calibration.
- Experience building reliable production solutions with a strong focus on testing, debugging, observability and CI/CD.
- Good understanding of data quality, metadata, provenance and versioning.
- Ability to diagnose quality issues across the complete AI pipeline and identify whether problems originate from source data, extraction, retrieval, ranking, grounding, prompting, orchestration or the
- Strong communication skills and the ability to explain AI quality limitations and technical trade-offs to both technical and non-technical stakeholders.
- Upper-intermediate or higher level of spoken and written English.
Nice to have
- Experience with ontologies, taxonomies or other semantic knowledge artefacts.
- Data Science or Machine Learning experience alongside strong software engineering capabilities.
- Spanish or Portuguese language skills.
Full description
Overview
We are looking for a Senior Python/AI Engineer to design and build the knowledge, retrieval and evaluation foundations behind accurate, grounded, safe and maintainable AI solutions.
In this role, you will combine strong Python backend engineering with hands-on AI/GenAI expertise, particularly in retrieval, RAG, document understanding and evaluation. You will work closely with subject matter experts to transform domain knowledge and source material into robust knowledge pipelines, evaluation frameworks and production-quality AI services.
This is a hands-on engineering role for someone who is rigorous about AI quality and evidence while being able to design, build, test and operate maintainable software in production.About Nortal:
We’re Nortal. We think big and create cutting-edge digital solutions with a global reach. And with 25 years of experience, 2,700+ professional experts, and half a billion people worldwide impacted by our work, we believe we’ve got the numbers to back up that statement.
Our global teams have played a significant role in many Fortune 500 companies’ projects and systems and have been the driving force of digital transformation for governments, healthcare institutions, and leading enterprises worldwide. We combine best-in-class strategic consulting with software engineering, data, and design practices to bring our visions to life.
About our client
Our client is a global mining company with a world-class portfolio of products that are used by a billion consumers every day. Our client relies on data and cutting-edge data science to enhance their operations. They also use advanced machine learning and artificial intelligence to optimize their processes.
Responsibilities
- Design and implement knowledge pipelines for AI solutions, covering source inventory and authority, extraction, metadata, canonical preparation, chunking and indexing.
- Build and improve hybrid retrieval and RAG solutions, including provenance, citations, access-aware retrieval, refresh and lineage controls.
- Create reusable evaluation frameworks and test packs for RAG assistants, document-processing pipelines and tool-using AI agents.
- Develop realistic test scenarios, regression cases, golden datasets, deterministic checks, targeted LLM judges and clear release thresholds.
- Analyse and improve end-to-end AI answer quality across extraction, source data, indexing, ranking, grounding, prompting, orchestration and model behaviour.
- Design precision-first approaches, including appropriate abstention and fallback mechanisms.
- Build and maintain Python backend services, APIs and test harnesses supporting AI components.