AI Engineer

Role overview

AI and machine-learning engineering

• Design, build, and maintain AI-powered applications and services; develop and deploy machine-learning and generative AI solutions.
• Build RAG systems, AI agents, reusable AI components, and frameworks; integrate foundation models and APIs into enterprise applications.
• Train, fine-tune, evaluate, and deploy models; develop feature-engineering and model-evaluation pipelines.
• Monitor production models, optimize inference performance and cost, and track model performance.

Generative AI and MLOps

• Develop LLM applications, workflows, prompt templates, agent frameworks, and orchestration pipelines.
• Implement vector search and knowledge retrieval systems; evaluate AI quality, safety, and reliability and mitigate hallucinations.
• Automate training, validation, and retraining pipelines; version datasets, features, and model artefacts using MLflow or similar tools.
• Deploy models through REST APIs or batch inference at scale on Databricks, Snowflake, or similar platforms.
• Monitor production models for drift and degradation; manage feature stores and data pipeline reliability.
• Implement experiment tracking and reproducibility across the model lifecycle.

Collaboration

• Work with solution architects, product managers, and engineering teams.
• Participate in code reviews and technical design discussions; support testing, deployment, and ongoing optimization.

Qualifications and experience

• Strong proficiency in Python and SQL.
• Experience with Scikit-learn, PyTorch and/or TensorFlow, Hugging Face, and AI services from leading cloud and model providers.
• Knowledge of supervised and unsupervised learning, model evaluation, feature engineering, and statistical concepts.
• Practical experience building RAG systems, AI agents, LLM-powered applications, and vector search solutions.
• Familiarity with LangChain, LlamaIndex, Semantic Kernel, or LangGraph.
• Experience with AWS, Docker/containerization, and CI/CD pipelines and DevOps practices; Kubernetes is preferred.
• At least 3 years in ML engineering or data science, including production model deployment and generative AI application experience.

Benefits

• Benefits include profit sharing, career progression and mentorship, financial and wellbeing programs, pension, private healthcare, life assurance, and employee discounts.
• The company offers educational assistance, incentivized certifications, generous holiday allowance, and enhanced family and special leave policies.
• Flexible or remote working is mentioned among the company's benefits.

About Version 1