About the Role Simplyai is seeking a highly motivated AI Engineer to join our growing Data & AI practice. The successful candidate will work on enterprise-grade AI, Generative AI, Machine Learning, and Intelligent Automation solutions for leading organizations across Australia and New Zealand. This role requires strong software engineering fundamentals combined with practical experience in machine learning, cloud-native application development, APIs, and modern AI frameworks. The ideal candidate should be comfortable building production-ready AI systems rather than only developing machine learning models. Key Responsibilities Design, develop, and deploy AI-powered applications and services. Build and maintain Generative AI solutions using Large Language Models (LLMs). Develop Retrieval-Augmented Generation (RAG) systems using vector databases and enterprise knowledge sources. Create scalable backend services and APIs to support AI applications. Build data ingestion, transformation, and feature engineering pipelines. Fine-tune, evaluate, and optimize machine learning and foundation models. Integrate AI solutions with enterprise systems, databases, and cloud platforms. Collaborate with solution architects, data scientists, and business stakeholders to deliver AI outcomes. Monitor and improve model performance, reliability, security, and scalability. Contribute to AI governance, responsible AI, and best-practice engineering standards. Required Skills Programming & Software Engineering Strong proficiency in Python. Good understanding of Java or TypeScript. Experience building REST APIs and microservices. Strong understanding of software engineering principles and design patterns. AI & Machine Learning Experience developing machine learning solutions using Scikit-learn, XGBoost, PyTorch, TensorFlow, or similar frameworks. Understanding of model training, evaluation, feature engineering, and deployment. Exposure to Generative AI, LLMs, prompt engineering, and AI agents. Understanding of RAG architectures and vector databases. Data Engineering Experience working with structured and unstructured datasets. Knowledge of SQL and data transformation pipelines. Familiarity with PostgreSQL, MySQL, SQL Server, MongoDB, or similar databases. Cloud & DevOps Experience with AWS, Azure, or Google Cloud. Knowledge of Docker and containerized deployments. Familiarity with CI/CD pipelines and Infrastructure as Code. Understanding of Kubernetes is desirable. Distributed Systems Understanding of event-driven architectures. Experience with Kafka, Redis, message queues, or asynchronous processing frameworks. Knowledge of scalable, distributed backend systems. Preferred Skills Experience with Azure OpenAI, OpenAI, Anthropic Claude, Gemini, or similar LLM platforms. Experience with LangChain, LlamaIndex, CrewAI, AutoGen, Semantic Kernel, or similar frameworks. Exposure to MLOps and AI deployment pipelines. Experience implementing AI solutions in enterprise environments. Knowledge of observability tools such as Grafana, Prometheus, and centralized logging platforms. Personal Attributes Strong analytical and problem-solving skills. Passion for Artificial Intelligence and emerging technologies. Excellent communication and stakeholder engagement skills. Ability to work independently and collaboratively in a fast-paced environment. Strong attention to detail and commitment to delivering quality solutions. Experience 1–3 Years Education Master’s Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field (Mandatory)
