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Parser
Parser
201 – 500 Darbinieki
Kvalificēta darbinieka vīzas sponsorsKonsultācijasMākslīgais intelektsMārketings
Parser ir 2018. gadā dibināts tehnoloģiju risinājumu un konsultāciju uzņēmums, kas palīdz organizācijām izmantot mākslīgo intelektu, datus un programmatūras inženieriju sarežģītu uzņēmējdarbības vajadzību risināšanā. Uzņēmuma darbība ietver pielāgotu programmatūras izstrādi, mākslīgā intelekta un datu izmantošanu, produktu dizainu un digitālās transformācijas konsultācijas. Parser sadarbojas ar tehnoloģiju līderiem, lai izstrādātu viņu mērķiem pielāgojamus risinājumus, apvienojot inženierzināšanas ar praktisku inovāciju un ilgtermiņa klientu partnerību pieeju.
LinkedIn
Vakances uzņēmumā

Senior AI Engineer, Spain (Remote)

Build Python services, APIs, and production AI features for LLM-powered search evaluation and relevance tools. The role supports Parser’s multinational retail client.

ESSpānijaAttālinātiPilna slodzeVecākais speciālistsNepieciešamā Valoda: angļuMākslīgā intelekta inženierisMašīnmācīšanās inženieris
AzureElasticsearchKafkaMākonisPythonSQL

Apraksts

  • Develop AI tools that help Search teams diagnose issues, test changes, and measure relevance faster.
  • Create AI features that turn search behavior, diagnostics, and quality data into prioritized insights.
  • Build backend services and APIs to collect and combine human relevance judgments.
  • Convert the existing offline evaluation tool into on-demand environments developers can run in CI/CD.
  • Set up automated checks for AI feature accuracy and reliability, including LLM-based relevance judgments.
  • Partner with product, design, data science, and Search teams to bring prototypes into production.
  • Connect tools to existing client platforms and APIs.
  • Track quality, latency, and cost, and record key decisions and trade-offs.
  • Share progress early through documentation, demos, and incremental pull requests.
  • Deliver work in line with agreed architecture and engineering practices.
  • Clearly explain decisions, risks, and trade-offs.
  • Review AI-generated output quality with Search and data science colleagues.
  • Test assumptions against real data, then refine solutions based on evidence.
  • Move from exploration to delivery while keeping early solutions appropriately simple.
  • Build tools that users can trust, understand, and adopt.
  • Choose deterministic rules, traditional machine learning, or human review when they suit the task better than an LLM.

Prasības

  • At least five years of professional experience in software engineering, data engineering, machine learning, or applied AI.
  • Practical experience building production LLM applications, including prompt design, structured outputs, and API integration.
  • Knowledge of embeddings, semantic and hybrid retrieval, and how they compare with lexical search.
  • Ability to assess models and approaches for quality, latency, security, and cost.
  • Experience creating offline evaluations and labelled-data benchmarks, including calibrating LLM judges against human labels.
  • Understanding of ranking measures such as nDCG, Precision at K, and MRR, as well as inter-rater agreement.
  • Awareness of hallucination, bias, drift, and privacy risks, with experience designing human review processes.
  • Strong SQL skills and experience working with large analytical datasets.
  • Familiarity with medallion architectures and connecting AI systems to data pipelines and APIs.
  • Strong Python skills, including experience developing maintainable backend services and APIs.
  • Sound testing practices for application, data, and AI components.
  • Experience with CI/CD, containers, and cloud infrastructure; Microsoft Azure is preferred.
  • Track record of delivering AI-enabled systems beyond standalone proofs of concept.
  • Ability to release iterative, user-focused solutions with appropriate reliability and safety measures.
  • Strong written and spoken communication, including explaining technical decisions and evaluation results to non-technical stakeholders.
  • Collaborative approach to delivering products across disciplines.
  • Preferred: Experience with retail or ecommerce search, including product taxonomy and long-tail queries.
  • Preferred: Practical experience with Elasticsearch or another production search platform, or with a vector database.
  • Preferred: Experience creating offline evaluation or A/B testing tools for search or recommendation.
  • Preferred: Experience developing human annotation or rating tools.
  • Preferred: Experience with event streaming such as Kafka, or with clickstream and web analytics data.

Priekšrocības

  • Highly competitive compensation package.
  • Medical insurance.
  • Flexible remote work.
  • Opportunity to join an organization experiencing triple-digit growth.
  • Opportunity to join a multicultural community of technology experts.

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