Senior AI Engineer

Das ist der Job

You will lead and deliver high-impact AI projects in collaboration with renowned clients in the European industry.

Darum lohnt es sich

ABOUT THE POSITION We are looking for an experienced AI Engineer to join our growing team and help design, and deploy robust AI software solutions for our clients. Advanced proficiency in Python and modern AI frameworks (PyTorch, TensorFlow), with a strong command of software design patterns, microservices architecture, and API design.

Ability to define and execute technical roadmaps paired with the experience mentoring engineering teams, conducting code/design reviews, and enforcing engineering excellence.

Benefits WHAT WE OFFER The opportunity to drive leading technology solutions in real‑world applications A high‑impact, client‑facing role with real responsibility and room for initiative A collaborative and dynamic work environment that values vision and impact Opportunity to actively contribute to a young company’s growth Flexible working hours and hybrid work models #J-18808-Ljbffr You will work hands‑on with multi-modal datasets, build predictive models, and develop AI‑driven products to support operations and gain efficiencies across business units.

Your role will combine hands‑on technical exposure with scientific curiosity and business relevance: from extracting insights from client interactions to building robust AI architectures which incorporate state‑of‑art technology and best practices to deliver client solutions.

This is more than a job — it’s an opportunity to help shape the future of our company and drive innovation. ABOUT SPHERE ENERGY We are a young, dynamic and technology‑driven company, committed to developing targeted AI‑driven solutions that revolutionize the development of products in the broad field of engineering.

Tasks KEY RESPONSIBILITIES Develop scalable AI‑driven software solutions and design end‑to‑end systems that integrate seamlessly with enterprise data and cloud infrastructure. Establish MLOps standards and reusable pipelines, ensuring clean, maintainable code for model training, deployment, and monitoring.

Translate complex business requirements into technical blueprints, bridging the gap between data science exploration and production engineering. Advise stakeholders on technical strategy, clearly communicating architectural tradeoffs regarding cost, latency, and performance.

Requirements QUALIFICATIONS Master’s in Computer Science, Data Science, Machine Learning, or a related field with experience in designing enterprise‑scale AI solutions. Deep architectural expertise across multiple AI domains—specifically within Generative AI, Computer Vision, and complex predictive modeling.

Expertise in designing robust MLOps ecosystems, including defining standards for CI/CD, model governance, security compliance, and infrastructure orchestration on cloud platforms (AWS, GCP, Azure). Expert at balancing critical architectural tradeoffs, including inference latency, scalability, data privacy, and total cost of ownership.