Senior Software Engineer – Profile Matching, Data & AI Platforms

O projekcie:
ALM Services Technology Group develops end–to–end Web and Mobile Solutions. We work closely with customers usually in long term relations.
Our mission is to create the best possible environment of work for our people, engage in innovative projects, and help to strengthen and develop new competences.

ALM was founded in 2009 in Poland. In 2022 we opened a branch in Budapest, and we are actively working on growing our team in Hungary.

ALM Services Technology Group comprises creative, open-minded individuals who develop innovative solutions daily to help our clients expand their businesses.

Our mission is to create the best possible environment of work for our people, engage in innovative projects, and help to strengthen our competences.

Since 2020 we have been cooperating with our partner, a multinational company working in a med-tech and analytics space. A recognized global leader still willing to challenge the status quo to improve patient care.

We have helped and supported them through various stages of growth. We have built multiple applications for them. We have the core team and know-how in place to help them grow further.

Wymagania:
We are looking for a Senior Software Engineer to lead the design and delivery of a production platform for collecting, matching, enriching, and delivering profile, entity, and content data from multiple sources.This is a hands-on leadership role. You will shape the architecture, drive technical direction, and own the design of profile/entity matching and LLM/ML systems across the platform, while remaining an active contributor to the codebase.This Role Is a Strong Fit If You Have- strong Python software engineering experience;- experience building or maintaining production systems that process multi-source content and profile/entity data;- hands-on experience with profile/entity matching: rule-based, fuzzy matching, graph-based approaches, embeddings, and ML;- experience designing multi-source data architectures and catalogues, including deduplication and survivorship strategies;- practical experience with LLM APIs, prompting, structured outputs, classification, summarisation, or extraction;- strong understanding of embeddings, vector similarity, semantic search, clustering, deduplication, and relevance scoring;- experience designing LLM/ML-based enrichment and matching systems end to end;- ability to make architecture decisions, evaluate technical trade-offs, and own scalability, reliability, and data quality;- ability to write clean, testable code, communicate technical decisions clearly, and provide technical leadership/mentoring;- experience using AI-assisted development tools while remaining responsible for quality, security, and correctness.Required Experience- 5+ years of experience in Software Engineering, Backend Engineering, Data Engineering, or Platform Engineering, including ownership of architecture and complex integrations;- strong commercial experience with Python;- experience designing and building profile/entity matching systems (rules, fuzzy matching, graph, embeddings, ML);- experience with data-integration pipelines feeding matching and enrichment systems;- knowledge of SQL and relational databases, preferably PostgreSQL;- experience with Snowflake;- experience with Airflow or similar orchestration tools;- experience designing systems around LLM APIs — prompting, structured outputs, embeddings, semantic retrieval;- experience with automated testing, CI/CD, version control, and code review;- strong communication skills, technical leadership experience, and fluent English.Technology ExpectationsPractical experience with several of the following:- Python and modern backend or data-processing frameworks;- profile/entity matching: rules engines, fuzzy matching, graph-based methods, embeddings, ML models;- multi-source data architecture and catalogs, deduplication, and survivorship design;- LLM APIs, prompting, structured outputs, classification, and extraction;- embeddings, vector databases, semantic search, clustering, and relevance scoring;- Airflow or equivalent orchestration tooling;- PostgreSQL and Snowflake;- data validation, schema evolution, reconciliation, and data-quality monitoring;- Docker, cloud infrastructure, CI/CD, and production deployment;- AI-assisted development tools such as GitHub Copilot, Claude Code, Cursor, or equivalent;- basics of data science and ML modeling applied to entity resolution;- MCP / agentic AI approaches.Nice to have:- rate-limit, quota, and API cost/usage budgeting across third-party integrations (e.g., social platforms, video platforms, and similar);- observability and SLO design — logs, metrics, dashboards, alerts, health checks;- Parquet, object storage, or analytical data platforms;- RAG, AI agents, LangChain, LangGraph, or LLM orchestration frameworks.

Codzienne zadania:
- build and maintain services for collecting, normalising, matching, enriching, and delivering profile, entity, and content data;
- design and implement profile/entity matching using rules, fuzzy matching, graph, embeddings, and ML, including deduplication and survivorship;
- design data-integration pipelines for ingestion, transformation, validation, storage, and delivery;
- design and implement AI enrichment using LLM APIs, prompting, structured outputs, embeddings, and vector similarity;
- build classification, summarisation, ranking, semantic matching, clustering, and deduplication mechanisms;
- define orchestration standards — incremental processing, backfills, and reprocessing using Airflow or similar tools;
- design data models and multi-source catalogs using PostgreSQL, Snowflake, or equivalent technologies;
- oversee integration of internal and third-party REST APIs (authentication, pagination, retries, caching, failures, API changes);
- guide backend and internal API development, including some full-stack / UI work in Node.js / React;
- define data-quality monitoring standards — freshness, completeness, and quality;
- own automated testing, CI/CD, code review, releases, and production-operations standards;
- write secure, testable, and maintainable Python code;
- collaborate with product, data, AI, and engineering teams;
- use AI-assisted development tools while remaining responsible for code quality and correctness;
- define the cross-feed matching architecture and the platform's technical direction;
- lead LLM / ML systems design and data architecture (multi-source catalogs, survivorship);
- own platform integration and pipeline handover;
- make decisions on scalability, reliability, performance, data quality, and cost (including API usage);
- define orchestration and data-quality monitoring standards, and production-readiness criteria;
- assess trade-offs, challenge solutions, identify technical risks, and provide technical leadership;
- take end-to-end ownership of complex platform components.