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# Remote DS/ML Engineer

- **Company:** Veeva Systems
- **Location:** London, UK (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Software as a Service, Information Systems, Python (Programming Language), Lex (Software), Machine Learning, Recommender Systems, Large Language Models, Kubernetes, Information Technology, Deployment Automation, Machine Learning Operations, Veeva, Natural Language Understanding
- **Published:** June 25, 2026
- **Apply:** https://find.jobs/jobs-near-me/remote-ds-ml-engineer-london/2837950089-2/

## About the Role

* 5+ years of developing data & analytics products like sentiment analyzers, translation engines, summarizers, or other language services
 * Expert in Machine Learning, Modeling, and development with Python
 * Expert in MLOps with at least one platform, e.g.: MLflow, Kubeflow, or end-to-end automation with SageMaker services
 * Ability to mentor others and work independently
 * Strong communication skills

Nice to Have

 * Production experience with any of the following is a plus: GenAI, LLMs, Recommendation Engines, AWS offerings for AI and ML including SageMaker, Bedrock, Lex
 * Knowledge of relevant regulations in AI/ML and incorporating compliance into MLOps processes
 * Bachelor's degree or higher in computer science, engineering, or information systems

## Description

Turn your data science skills into tangible impact! Develop innovative services that make a difference. We're a rapidly growing company looking for teammates to solve complex problems and bring solutions to life in large-scale software products.

The Veeva Link engineering team seeks Data Scientists and Machine Learning Engineers to participate in all aspects of building and operating AI language features that help our global clients bring products to market faster.

\n

What You'll Do

 * Work within a cross-functional application team to build scalable language services in the range of NLP, NLU, and NLG for live SaaS products
 * Help develop and groom an experiment backlog
 * Build models that solve real-world problems
 * Optimize models for production throughput and uptime requirements
 * Automate deployments, testing, and monitoring (MLOps)


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