AI Engineer

The role 

Ideal candidates will have a strong foundation in AI/ML, cloud platforms, and data engineering. They will use these skills not as buzzwords but as tools for building intelligent systems that support real-world digital operations. This role offers the opportunity to work at the intersection of cloud technologies, AI, and commerce enablement, driving value through scalable insights and automation. 

We are seeking an AI Engineer to join our growing data and intelligence team. If you enjoy solving complex business problems using advanced AI/ML tools and cloud-native solutions—while working with diverse data from digital commerce, marketing, and content platforms—read on! 

This role will focus on activating an AI-driven commerce transformation, delivering scalable intelligence across digital marketing, eCommerce, and content performance systems. 

Role responsibilities 

• Design, build, and scale AI/ML solutions using cloud-native tools across GCP, Azure, and AWS to support use cases like anomaly detection, content classification, predictive KPIs, and intelligent workflow automation. 
• Leverage Databricks to develop unified data pipelines, orchestrate model training/inference workflows, and support real-time decisioning across multiple systems. 
• Collaborate with data analysts, platform engineers, and business stakeholders to operationalize models that drive measurable impact (e.g., SLA improvements, user behavior insights, conversion predictions). 
• Build AI-powered dashboards and explainable insights using Power BI, Tableau, or Looker—enabling business users to make informed decisions backed by models. 
• Apply advanced techniques (e.g., NLP, time series forecasting, clustering) to optimize platform stability, personalization, and operational readiness. 
• Integrate AI solutions with platform telemetry (e.g., observability logs, ticket metadata, content usage stats) to surface proactive insights and reduce downstream escalations. 
• Champion data integrity, reusability, and responsible AI practices across the lifecycle of development, deployment, and monitoring. 
• Model Monitoring & Feedback Loops: Establish performance monitoring, alerting, and drift detection mechanisms to ensure model integrity and business alignment post-deployment. 
• LLM/GenAI Enablement: Support conversational analytics through GenAI/LLM interfaces for natural-language querying of marketing and commerce data. 
• Real-Time Decisioning: Architect real-time inferencing capabilities using event-driven tools (e.g., Pub/Sub, Kafka) and integrate with commerce or marketing platforms for next-best-action recommendations. 

Minimum qualifications  

We want all new hires to succeed in their roles at Valtech. That's why we've outlined the job requirements below. To be considered for this role, it's important that you meet all minimum qualifications. If you do not meet all of the preferred qualifications, we still encourage you to apply. 

• 5+ years of experience building and deploying machine learning models in production environments. 
• Proficiency with Databricks and cloud-native ML tooling across GCP (Vertex AI), Azure ML, or AWS SageMaker. 
• Strong programming skills in Python and SQL; hands-on experience with frameworks such as scikit-learn, TensorFlow, PyTorch, or MLflow. 
• Proven experience building data pipelines, training workflows, and model-serving systems that integrate into enterprise environments. 
• Experience creating AI-integrated dashboards or tools using Power BI, Tableau, or Looker. 
• Demonstrated ability to collaborate with cross-functional teams including product owners, analysts, QA, and DevOps. 
• Experience deploying models in GCP, especially with Vertex AI Pipelines, BigQuery ML, and GenAI Studio or similar. 
• Familiarity with data product thinking—turning ML insights into reusable APIs, dashboards, or automated workflows that business teams can use daily. 
• Hands-on with model explainability, governance, and bias detection tools (e.g., What-If Tool, SHAP, Model Cards). 

Preferred qualifications  

• Exposure to multi-platform digital ecosystems (e.g., commerce platforms, content management, observability tooling, customer data platforms). 
• Familiarity with orchestration tools like Airflow or event-based workflows using Pub/Sub, Kafka, or Event Grid. 
• Experience integrating ML models into monitoring systems or observability stacks (e.g., ELK, Prometheus, Grafana). 
• Understanding of data governance and MLOps best practices (e.g., drift detection, version control, explainability). 
• Strong communication skills with the ability to translate technical findings into business-impact narratives. 
• Previous experience contributing to AI strategy, capability roadmaps, or solution architecture in enterprise settings. 
• LLM/GenAI experience (especially for insights extraction and conversational UX). 
• Experience in retail or multi-brand global ecosystems. 
• Worked in environments requiring cross-platform telemetry aggregation—logs, content stats, error traces, SLA metadata, etc. 
• Experience with multi-region data architectures and compliance-aware ML deployment. 

Your process 

After submitting your application, you will hear from a member of our Talent Acquisition team if your skills and experience match the position. Be mindful of recruitment fraud by only replying and engaging with email contacts that end with the '@valtech.com' or ‘@kinandcarta.com’ domain. 

We strive to create an inclusive environment for all candidates. If you have any specific requirements or need reasonable adjustments to participate in the application or interview process, please let us know at kincarta-amer.ta.team@valtech.com.