Lead ML Engineer – Classical ML & GenAI/RAG

Location: Remote, United Kingdom

Company

hirify.global is a technology consulting and supply chain solutions company founded in 2008, delivering technology products and services for clients.

What you will do

• Design, develop, and productionize machine learning solutions for real-world business problems, including classification, regression, forecasting, clustering, and anomaly detection.
• Perform end-to-end data preparation, feature engineering, model development, validation, tuning, and evaluation against measurable business outcomes.
• Develop reusable, production-quality Python and SQL code and maintain automated training and inference pipelines.
• Deploy and support models across cloud and on-premise environments, owning reproducibility, versioning, monitoring, troubleshooting, and retraining.
• Analyze existing ML codebases, establish baselines, identify improvement opportunities, and implement measurable enhancements.
• Develop and support GenAI, LLM, and RAG applications while providing hands-on technical leadership and collaborating with data scientists, engineers, and business stakeholders.

Requirements

• 5+ years of hands-on experience in Machine Learning, Data Science, ML Engineering, or closely related roles.
• Strong practical experience with classical machine learning and proven experience delivering supervised and/or unsupervised ML solutions in production.
• Strong Python and SQL skills, with experience producing reusable, production-quality code.
• Experience developing ML training and inference pipelines, deploying and supporting models in production, and applying model monitoring, troubleshooting, versioning, reproducibility, and retraining.
• Strong understanding of data preparation, feature engineering, model validation, data leakage prevention, hyperparameter tuning, model evaluation, and baseline comparison.
• Experience with Git, version control, code reviews, and working hands-on within an existing codebase.
• Recent hands-on experience with GenAI, LLM, and RAG applications, including evaluating and debugging RAG/LLM solutions.
• Strong problem-solving and technical leadership capabilities.

Nice to have

• Experience deploying ML solutions across cloud and on-premise environments and using ML/MLOps platforms and tooling.
• Experience with Docker, Kubernetes, or similar deployment technologies.
• Experience with AWS, Azure, or GCP.
• Experience with model serving and API-based ML deployment.
• Experience with LLM evaluation frameworks, RAG architectures, embeddings, vector databases, retrieval pipelines, and prompt/model evaluation.

Culture & Benefits

• Remote, full-time employment.
• Work across technology consulting and supply chain solutions.
• Hands-on ownership of production ML systems and technical guidance for other ML engineers and data scientists.