Position: Lead AI/ML Engineer | Marketing platform
Company: Xenoss
Category: Data Science
Experience required: 3y
English level: intermediate

What will be your goal

Your goal will be to ensure technical excellence of AI/ML solutions built by the Data Science team, that should be delivered as standalone services that can be efficiently used by other teams

What you will do
- You’ll be designing and building new AI/ML-based creative automation services and ensuring their smooth integration into the solution
- You’ll be actively communicating with the product team on the feature requirements, including image/video processing (CV), marketing visuals auto-generation, campaign performance prediction and other AI-related tracks
- You’ll be transforming product requirements into reliable, scalable, and maintainable solutions
- You’ll be working on critical architectural and technical tasks along with the Solution Architect
- You’ll be helping the Product Owner to groom the backlog and prioritize the product roadmap

Must-have skills
- Strong understanding of data structures & algorithms
- Proficiency in Python
- Experience in building applications/services based on open source ML models
- Goal orientation & Problem-solving
- Working proficiency in English

Do not hesitate to apply if you are missing some specific experience. We will be happy to help you with gaining one.

Nice-to-have skills
- Leadership experience
- Node.js
- Experience with a microservice architecture

Client: Successful British ad-tech company with big global clients including Virgin, Nestle, Adidas, J&J, Uber, Nissan, Shell, HSBC.

Project: The product we are building is an AI-powered SaaS platform for managing marketing campaigns and generating promotional visuals (mainly digital ads), that is used by global top brands.
Building technology for smart advertising solutions is one of the biggest modern challenges that currently exist. Making ads to be more efficient, more relevant & less intrusive will benefit both advertisers and users. By collecting and analyzing (big) data on user reaction to the creatives, the platform learns & provides the most relevant creatives for each user segment (audience).

Tech stack: Node.js, Express.js, React.js, Redux, Rematch, Typescript, Python, Java, RabbitMQ, MongoDB, ClickHouse OLAP DBMS, Redis (cache), Elasticsearch, AWS, Docker, Kubernetes