Senior AI Engineer

🔥 6 hours ago

🇪🇸 Spain – Remote

⏰ Full Time

🟠 Senior

🤖 AI Engineer

👻 Ghost score 11%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Parser

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2018

💼 Consulting

📣 Marketing

🤖 Artificial Intelligence

Consulting • Marketing • Artificial Intelligence

Parser is a technology solutions company that elevates organizations through innovative AI-driven strategies and expert engineering services. The company partners with technology leaders to achieve key business goals, focusing on adaptability, customization, and collaboration. With a commitment to fostering innovation and long-term partnerships, Parser delivers comprehensive solutions in software engineering, AI and data utilization, product design, and digital transformation consulting.

📋 Description

• Build AI-enabled tools that help Search teams find problems, test changes and measure relevance more quickly • Design and build AI-enabled features that turn search behaviour, diagnostics and quality data into prioritised, actionable insights • Build backend services and APIs for tools that collect and combine human relevance judgements • Turn the existing offline evaluation tool into on-demand environments that developers can run in the CI/CD pipeline • Define and automate checks on the accuracy and reliability of AI features, including LLM-based relevance judgements • Work with product, design, data science and Search colleagues to turn prototypes into production releases • Integrate with existing client platforms and APIs • Monitor quality, latency and cost, and document decisions and trade-offs • Share work early through documentation, demonstrations and incremental pull requests • Own deliverables within agreed architecture and engineering practices • Communicate decisions, risks and trade-offs clearly • Assess the quality of AI-generated outputs with Search and data science colleagues • Experiment quickly, test assumptions against real data and iterate using evidence • Balance exploration with delivery and avoid unnecessary complexity early on • Design for trust, clarity and adoption by tool users • Recognise when deterministic rules, traditional machine learning or human review are more appropriate than an LLM

🎯 Requirements

• Five or more years of professional experience across software engineering, data engineering, machine learning or applied AI • Hands-on experience building LLM applications in production, including prompt design, structured outputs and API integration • Understanding of embeddings, semantic search and hybrid retrieval, and their trade-offs against lexical search • Ability to compare models and approaches on quality, latency, security and cost • Experience building offline evaluations and benchmarks with labelled datasets, and using LLMs as judges calibrated against human labels • Understanding of ranking metrics such as nDCG, Precision at K and MRR, and of inter-rater agreement • Awareness of hallucination, bias, drift and privacy risks, and experience designing human review • Strong SQL skills and experience with large analytical datasets • Familiarity with medallion architectures and integrating AI with data pipelines and APIs • Strong Python skills, including maintainable backend services and APIs • Good testing discipline across application, data and AI components • Experience with CI/CD, containerisation and cloud infrastructure, ideally Microsoft Azure • Demonstrated experience delivering AI-enabled systems beyond isolated proofs of concept • Proven ability to ship iterative, user-focused solutions with appropriate attention to reliability and safety • Strong written and verbal communication skills, including explaining technical choices and evaluation results to non-technical stakeholders • A collaborative approach to multidisciplinary product delivery • Nice to have: Experience in retail or ecommerce search, including product taxonomy and long-tail queries • Nice to have: Hands-on experience with Elasticsearch or another production search platform, or with a vector database • Nice to have: Experience building offline evaluation or A/B testing tooling for search or recommendation • Nice to have: Experience building human annotation or rating tools • Nice to have: Experience with event streaming, such as Kafka, or clickstream and web analytics data

🏖️ Benefits

• Highly competitive compensation package • Medical insurance • Flexible remote work • Opportunity to join an organization with triple-digit growth • Opportunity to form part of a multicultural community of tech experts