Senior Artificial Intelligence/Machine Learning Engineer (LLM)

TL;DR

Senior Artificial Intelligence/Machine Learning Engineer (LLM): Designing and implementing MCP servers and secure endpoints for LLMs, and deploying models into production while integrating with various systems like EHR modules. Focus on managing model performance, building machine learning pipelines, ensuring AI system security, and monitoring AI workload costs.

Location: Remote from Poland

Company

hirify.global is a custom product engineering company providing technology solutions to multinational organizations and scaling startups.

What you will do

• Design and implement MCP servers that expose internal data/services to LLMs.
• Build secure, structured endpoints for model context access and integrate with inference APIs.
• Implement and operate a vector search engine.
• Deploy models into production (cloud, on-premise, or hybrid) and integrate with upstream/downstream systems.
• Monitor model performance in live settings for accuracy, drift, bias, fairness, and reproducibility.
• Build and maintain machine learning pipelines and connect AI workloads to core datasets.

Requirements

• Proficiency in Python and strong familiarity with ML frameworks/libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Experience building APIs, services, or microservices.
• Knowledge of vector databases or search systems.
• Experience with LLM application patterns: RAG, embeddings, prompt orchestration, and tool calling.
• Experience with basic MLOps practices: model deployment, monitoring, pipeline automation, CI/CD.
• Demonstrated ability to deploy models into production environments (cloud environments like AWS, Azure, GCP or containerized/micro-services infrastructure). GCP experience is strongly preferred.
• Bachelor’s degree (or equivalent) in Computer Science, Data Science, Statistics, Engineering, or a related field.
• 5+ years of platform/infrastructure engineering experience, with demonstrable recent work on LLM-based systems.

Nice to have

• Experience in healthcare, behavioral health, EHR systems, or regulated industries.
• Familiarity with MLOps practices: CI/CD for models, model monitoring, drift detection, model governance.
• Experience with NLP (clinical text) or computer vision (imaging) tasks.
• Familiarity with cloud-native services for ML (e.g., AWS SageMaker, Azure ML, GCP AI Platform) and related infrastructure (Docker, Kubernetes).
• Awareness of AI ethics, bias/fairness issues, model interpretability techniques.
• Experience mentoring others or leading small tech initiatives.

Culture & Benefits

• Work alongside top professionals in a friendly, open-door environment.
• Opportunity to take on large-scale projects with global impact and expand your expertise.
• Tailored learning opportunities including internal events (meetups, conferences, workshops), Udemy access, language courses, and company-paid certifications.
• Explore diverse domains through internal mobility and gain hands-on experience with cutting-edge technologies.
• Enjoy full remote working possibilities.
• Company-paid medical insurance, mental health support, and financial & legal consultations.