AI & ML Site Reliability Engineer - **Must be in Ireland** (remote/hybrid)

AI & ML Site Reliability Engineer - **Must be in Ireland** (open to Remote or Hybrid)

We are seeking an experienced AI & ML Site Reliability Engineer to design, build, and maintain infrastructure supporting AI, machine learning, and data-driven solutions. This role involves architecting scalable systems, managing AI deployments, and optimizing data platforms to enable impactful business outcomes. You’ll work closely with data scientists, engineers, and cross-functional teams to ensure reliability, scalability, and efficiency throughout the AI lifecycle.

Key Responsibilities

• AI/ML Infrastructure Design: Build scalable, secure, and efficient AI systems for training and deploying ML models.
• Data Platform Development: Develop and maintain a centralized big data platform to support analytics and AI solutions.
• Vector Databases & Knowledge Graphs: Implement and manage databases for high-dimensional data and structured/unstructured data integration.
• Model Deployment & Optimization: Deploy, monitor, and optimize ML models for performance and scalability.
• CI/CD Pipelines: Design and implement CI/CD processes tailored for ML workflows, ensuring reproducibility and automation.
• RAG & Advanced Techniques: Develop retrieval-augmented generation (RAG) systems, including advanced reasoning systems like GraphRAG.
• Monitoring & Governance: Establish protocols for model performance monitoring, retraining, and ethical AI governance.

Qualifications

• Education: Bachelor’s degree in Computer Science, Data Science, or related field.
• Experience:
• 5+ years in site reliability, DevOps, or ML Ops roles.
• Experience with cloud platforms (AWS, GCP, Azure) and AI/ML tools (e.g., TensorFlow, PyTorch).
• Proven track record deploying ML models (regressions, neural networks, etc.) in production environments.

• Technical Expertise:
• Proficiency in Python, Bash, and related ML libraries.
• Experience with data tools like Apache Spark, Hadoop, or Airflow.
• Knowledge of vector databases (e.g., Pinecone, Milvus) and knowledge graph tools (e.g., Neo4j).
• Familiarity with containerization (Docker, Kubernetes) and CI/CD for ML pipelines.
• Behavioral Skills:
• Problem-solving mindset and intellectual curiosity.
• Strong collaboration and communication abilities.
• Entrepreneurial spirit and adaptability.

Preferred Qualifications:

• Master’s degree in a relevant field.
• Experience in private LLM fine-tuning, edge AI deployments, or advanced data infrastructure.
• Knowledge of AI ethics and risk management.