Ai Ml Engineer

TL;DR

AI ML Engineer (SaaS/Medtech): Designing, developing, and deploying AI-powered features leveraging machine learning models and large language models to enhance a life sciences quality management platform with an accent on semantic search, retrieval-augmented generation, and cloud integration. Focus on building production-ready ML features, optimizing model performance, and collaborating cross-functionally in a fully remote European environment.

Location: Fully remote across EMEA (Europe)

Company

hirify.global helps life science companies digitize, automate, and scale their quality and compliance processes through a SaaS eQMS platform.

What you will do

• Develop and implement machine learning models including LLM-based features to enhance product capabilities.
• Analyze structured and unstructured data with advanced techniques like semantic search and RAG.
• Collaborate with product managers, developers, and QA teams to integrate and deploy AI features.
• Continuously monitor, evaluate, and optimize model performance in production.
• Conduct data preprocessing, feature engineering, and model validation.

Requirements

• Location: Must be based in Europe or EMEA region for remote work
• Bachelor's or Master's degree in relevant fields with 7+ years software engineering experience and 3+ years in AI/ML roles.
• Strong proficiency in Python, TensorFlow, PyTorch, scikit-learn, pandas, and NumPy.
• Experience working with LLMs, semantic search, RAG, and cloud platforms (AWS preferred).
• Excellent analytical, problem-solving, and communication skills.

Nice to have

• Experience shipping ML features in production and fine-tuning LLMs.
• Background in SaaS or life sciences sectors and knowledge of vector databases.
• Familiarity with multiple cloud platforms (AWS, Azure, GCP) and ML deployment processes.

Culture & Benefits

• Fully remote work with flexible hours across EMEA.
• Competitive compensation with performance incentives.
• Collaborative international team culture with clear career paths.
• Continuous professional training and development opportunities.
• Focus on work-life balance, creativity, and data-driven decision making.