Ai specialist (machine learning)

The AI Specialist is responsible for architecting and operating an end-to-end data and AI platform, including a centralised data warehouse, ETL/ELT pipelines, ML systems, and LLM-based applications. The role involves building RAG architectures with vector databases and embedding pipelines, applying MLOps and LLMOps practices, and orchestrating data and AI workflows using n8n for production-scale deployment. Key Responsibilities: Data Platform & Engineering Design, build, and maintain a centralised data warehouse to support analytics, ML, and Large Language Model (LLM) workloads Develop and manage ETL/ELT pipelines to ingest, transform, and curate data from internal and external sources Ensure data quality, reliability, security, and governance across all data systems Machine Learning & LLM Development Design, train, evaluate, deploy, and maintain machine learning models for predictive and analytical use cases Develop and integrate LLM solutions for enterprise and business applications Design and implement Retrieval-Augmented Generation (RAG) pipelines using structured and unstructured data Build and manage vector databases and embedding pipelines for semantic search and RAG systems MLOps, LLMOps & Automation Deploy and maintain ML and LLM services in production environments Monitor model performance, data drift, prompt effectiveness, and retraining or re-indexing requirements Build and manage automated workflows using n8n to orchestrate data pipelines, ML processes, LLM inference, and system integrations Integration, Collaboration & Governance Integrate AI, ML, and LLM solutions with applications via APIs and services Collaborate with stakeholders to translate business requirements into scalable AI-driven solutions Document system architecture, data pipelines, ML models, and LLM/RAG designs Ensure compliance with data privacy, security, and responsible AI practices Requirements: Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related field At least 6 years of experience in data engineering, machine learning, or AI system development Hands-on experience deploying ML and LLM solutions in production environments. Experience building and deploying machine learning models using frameworks such as Scikit-learn, Tensor Flow, or Py Torch Practical experience working with LLMs (e.g. Open AI, Azure Open AI, Anthropic, or open-source models) Experience designing or implementing RAG architectures Experience with workflow automation tools (e.g. n8n, Airflow, or similar) Proficiency in Python and SQL Experience with data warehouse technologies (e.g. Big Query, Snowflake, Redshift, Azure Synapse) Understanding of data modelling, ETL/ELT processes, MLOps, and LLMOps concepts Hands-on experience with vector databases, embedding models, semantic search, and document chunking strategies Familiarity with LLM orchestration frameworks such as Lang Chain, Llama Index, or Semantic Kernel Experience with prompt engineering, evaluation, and guardrails Familiarity with APIs, microservices, and event-driven systems Experience with BI tools (Power BI, Tableau, Looker) Understanding of data governance, privacy regulations, and responsible AI principles