AI Engineer

We are seeking a hands-on AI/ML Engineer to build, deploy, and optimize machine learning and Generative AI solutions. The role focuses on developing production-grade AI applications, integrating AI models into business systems, and supporting the full AI development lifecycle. The ideal candidate is passionate about modern AI technologies, software engineering best practices, and delivering reliable AI solutions at scale. Key Responsibilities AI Application Development - Design, build, and maintain AI-powered applications and services. - Develop and deploy Machine Learning and Generative AI solutions. - Build Retrieval-Augmented Generation (RAG) systems and AI agents. - Integrate foundation models and APIs into enterprise applications. - Create reusable AI components and frameworks. Machine Learning Engineering - Train, fine-tune, evaluate, and deploy ML models. - Develop feature engineering and model evaluation pipelines. - Implement model monitoring and performance tracking. - Optimize model inference performance and cost efficiency. Generative AI Engineering - Develop LLM-based applications and workflows. - Build prompt templates, agent frameworks, and orchestration pipelines. - Implement vector search and knowledge retrieval systems. - Design evaluation frameworks for AI quality, safety, and reliability. - Improve hallucination mitigation and response accuracy. MLOps & Deployment - Design and automate ML model training, validation, and retraining pipelines. - Version and manage datasets, features, and model artefacts using tools such as MLflow or similar. - Deploy and serve ML models via REST APIs or batch inference at scale on Databricks, Snowflake or similar. - Monitor model performance in production, detecting drift and degradation. - Manage feature stores and ensure data pipeline reliability for ML workloads. - Implement experiment tracking and reproducibility across the model lifecycle. Collaboration - Work closely with Solution Architects, Product Managers, and Engineering Teams. - Participate in code reviews and technical design discussions. - Support testing, deployment, and ongoing optimisation activities. Programming & Engineering - Strong proficiency in: - Python - SQL AI & Machine Learning - Experience with: - Scikit-learn - PyTorch and/or TensorFlow - Hugging Face - Main AI services from leading cloud service and model providers - Supervised and unsupervised learning - Model evaluation - Feature engineering - Statistical concepts Generative AI - Practical experience building: - RAG systems - AI agents - LLM-powered applications - Vector search solutions - Familiarity with: - LangChain - LlamaIndex - Semantic Kernel - LangGraph Cloud & Infrastructure - Experience with AWS. - Docker and containerization. - Kubernetes (preferred). - CI/CD pipelines and DevOps practices. Professional Experience - 3+ years in ML engineering or data science, with demonstrable experience deploying models to production and building Gen AI applications including RAG systems, LLM integration, or agentic workflows - Demonstrated experience delivering AI solutions into production environments.