AI Engineers – Search & RecSys & GenAI
As a part of our AI teams you'll work with talented AI engineers at every level to improve our search and recommendation solutions within AppGallery. This role allows you to directly affect user experience of millions of AppGallery users.
You'll have a chance to thrive in a multinational environment. Within this role you can expect to carry out following tasks;
- Design, develop, and optimize CTR prediction, graph-based models and ranking models for efficient and accurate search & recommendation functionality in app store domain.
- Conduct feature engineering and data analysis to drive better model performance and insights.
- Explore and integrate generative AI techniques, LLM fine-tuning methods such as LoRA, QLoRA, or Prompt Tuning into search and recommendation systems.
- Develop agentic search and recommendation solutions for autonomous decision-making in search and recommendation pipelines.
- Design, build and deploy scalable ML services on Huawei's MLOps platform to address business requirements.
- Collaborate with cross-functional teams to analyze complex data requirements and design innovative software solutions.
- Conduct code reviews to ensure high-quality, maintainable, and efficient code following industry best practices.
- Mentor and guide junior engineers in the areas of search, information retrieval, deep learning, and NLP.
- Stay updated with the latest advancements in information retrieval, ranking, deep learning, NLP, generative AI and agentic search, and integrate them into our products and services
- Produce academic outputs in the form of papers, patents, and technical talks.
Requirements
- Minimum MS degree or PHD degree with focus on RecSys, IR and DL, preferably in a computer science & engineering or related fields.
- Minimum 3 years of experience preferably in industry.
- Strong Python programming skills with proven experience crafting, prototyping, and delivering machine learning solutions into production.
- Experience with popular deep learning frameworks (TensorFlow, PyTorch) and other ML libraries.
- Proven expertise in LLM-based development, RAG pipelines, and multi-agent systems.
- Publication records in journals or conferences related with RecSys, IR, NLP especially in the areas of ctr prediction, vector retrieval, semantic search and ranking.
- Experience in integrating deep models, language models (LMs), generative AI models, and ranking models for large-scale recommendation engines using these specialized techniques.
- Previous project experience in search or recommendation systems domain, including agentic search and recommendation approaches, is a big plus.
- Java service development experience using Spring and related topics & technologies is a plus (RESTful services, Redis, ElasticSearch, RDBMS)
- Fluency in English is important, reading/writing skills in Russian and/or Arabic is a plus.
Am I right for the team?
- If you're excellent in analysis, modeling and problem-solving, and can see the essence of problems from complex data, you can be perfect fit for the task at hand.
- If you're easy to communicate, open for suggestions and improvements, can work independently, pro-actively and well aligned then you can be perfect fit for our team culture.
