Senior AI/ML Engineer

Sperton Global AS•Sweden, ME

1d•Hybrid

About The Position

We are hiring for one of our clients, a fast-growing AI-driven product company operating at the intersection of AI, e-commerce, and retail technology. They are looking for a Senior AI/ML Engineer to design, build, and scale machine learning systems powering real-world production use cases. The role focuses on building and deploying ML models and AI systems that operate at scale, including LLM-powered features, intelligent product matching, and real-time data-driven experiences. You will work closely with product, engineering, and leadership teams to translate business needs into production-ready AI solutions and help define best practices for machine learning and AI engineering across the organization.

Requirements

5+ years of software engineering experience, including at least 2 years in ML systems
Strong Python skills and experience with ML frameworks such as PyTorch and/or TensorFlow
Proven experience building and deploying ML models into production environments
Strong understanding of MLOps practices, including monitoring, deployment, and lifecycle management
Experience with LLMs, RAG systems, prompt engineering, or evaluation frameworks
Strong problem-solving skills and ability to work with ambiguous technical challenges
Excellent communication skills and ability to explain technical concepts to non-technical stakeholders
Comfortable working independently in a fast-paced startup environment
Fluent in English

Nice To Haves

Experience in e-commerce, recommendation systems, or search/matching systems
Experience with high-scale distributed systems or real-time data pipelines
Experience working in product-driven AI teams
Exposure to retail, media, or marketplace platforms

Responsibilities

Design, build, and train machine learning models for production use cases
Develop and maintain scalable ML infrastructure and pipelines with MLOps best practices
Deploy and monitor ML models in production environments, ensuring reliability and performance
Integrate LLMs and AI APIs into production systems (RAG, prompt engineering, evaluation, experimentation)
Improve model performance through iterative refinement and continuous monitoring
Collaborate with product and engineering teams to translate business requirements into technical solutions
Contribute to architectural decisions and AI/ML best practices within the organization
Research and evaluate emerging AI technologies and determine readiness for production use