Premium HR Solutions is a human resource consultancy company which offers strategic and effective HR solutions for small to medium sized businesses.
Role Summary
- The Applied AI Engineer will design, build, and operationalise artificial intelligence and machine learning solutions that create measurable business value across the Group's operations.
- The role demands a practitioner who can bridge cutting-edge AI research and real-world product deployment — from data ingestion and model development to production monitoring and continuous improvement.
Key Responsibilities
- Design and implement end-to-end AI/ML pipelines, covering data collection, preprocessing, feature engineering, model training, evaluation, and deployment.
- Develop and fine-tune large language models (LLMs) and generative AI applications tailored to internal business processes, including document intelligence, predictive maintenance, and workflow automation.
- Build and maintain Retrieval-Augmented Generation (RAG) systems, vector databases, and embedding pipelines for enterprise knowledge management.
- Integrate AI capabilities into existing products and platforms via RESTful APIs, microservices, and cloud-native architectures.
- Establish and enforce MLOps best practices: version control for models (MLflow, DVC), CI/CD for ML pipelines, and automated retraining triggers.
- Monitor model performance in production; detect and mitigate data/model drift, bias, and reliability issues.
- Collaborate with the Technical Product Manager and business stakeholders to identify and scope AI use cases with clear ROI.
- Conduct rigorous experimentation (A/B testing, hypothesis testing) to validate model improvements.
- Prepare technical documentation, model cards, and explainability reports for both engineering and non-technical audiences.
- Stay abreast of the latest AI research, frameworks, and tools; champion responsible and ethical AI practices within the organisation.
- Support data engineering efforts including ETL pipeline design, data warehouse integration, and data quality assurance.
- Mentor junior team members and contribute to the Group's internal AI capability-building programmes.
- Support the Head of Digital Solutions in budget planning and vendor procurement processes.
Qualifications and Experience
- Bachelor's or Master's Degree in Computer Science, Data Science, Artificial Intelligence, Statistics, or a related quantitative field.
- Minimum 3 years of hands-on experience building and deploying production-grade ML/AI systems.
- Strong proficiency in Python; experience with ML frameworks such as PyTorch, TensorFlow, or JAX.
- Practical experience with LLMs (OpenAI, Anthropic Claude, Mistral, LLaMA) and prompt engineering techniques.
- Familiarity with vector stores (Pinecone, Weaviate, ChromaDB) and RAG architectures.
- Experience with cloud ML platforms: AWS SageMaker, Azure ML, or Google Vertex AI.
- Solid understanding of MLOps tools: MLflow, Kubeflow, DVC, or equivalent.
- Knowledge of SQL and NoSQL databases; experience with data pipeline tools (Apache Airflow, dbt, Spark) is an advantage.
- Understanding of containerisation (Docker, Kubernetes) and cloud-native deployment.
- Strong statistical foundations: probability, hypothesis testing, regression, classification, and clustering.
- Demonstrated ability to communicate model insights to non-technical stakeholders.
Method of Application
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