ML Engineer (Search)

TL;DR

ML Engineer (Search): Developing AI-powered search and personalized recommendation systems for a global investment management firm with an accent on search ranking, deep learning, and production-grade ML solutions. Focus on optimizing model latency, implementing MLOps pipelines, and conducting end-to-end A/B testing to improve user engagement.

Location: Worldwide (Remote, Hybrid, or Onsite)

Salary: EUR 2,800 – 4,550 per month (gross)

Company

International IT outsourcing company providing high-end technical solutions for global leaders in fintech, healthcare, and retail.

What you will do

• Design and build deep learning systems for search ranking, session-based recommendations, and multi-objective personalization.
• Implement geographic context into ranking systems and optimize the recommendation of pickup points.
• Lead end-to-end evaluations, from offline metrics to the design and analysis of online A/B tests.
• Collaborate with backend engineers to transition models from prototype to production, optimizing for latency and serving.
• Own the full ML production lifecycle, including monitoring for concept drift and building retraining pipelines.
• Translate business goals into technical ML objectives and non-functional requirements.

Requirements

• 5+ years of experience building and deploying deep learning models in production.
• Direct professional experience with search, NLP, ranking, or recommendation systems.
• Expert proficiency in Python (PyTorch, Pandas, NumPy, Scikit-learn) and SQL (PySpark).
• Proven ability to design ML systems from scratch, covering data analysis, annotation, and production serving.
• Experience with MLOps tools and practices to manage the model lifecycle.
• English: Upper-Intermediate (B2) or above

Nice to have

• Experience fine-tuning and deploying LLMs or SLMs for query understanding and relevance.
• Specialized depth in geocoding, autocomplete relevance, or geospatial products.
• Experience building products for developing markets with limited map and address data.
• BigQuery or Databricks certifications.

Culture & Benefits

• Flexible work arrangements: choose between fully remote, hybrid, or office-based work.
• Structured professional growth with mentoring and adaptation systems for new employees.
• Access to a comprehensive corporate training portal and internal knowledge base.
• Private health insurance and compensation for sports activities.
• Compensation for professional certifications (AWS, PMP, etc.) and a referral program.
• Active corporate life with social events, parties, and provided office perks.