Full Stack Developer

Location: SydneyEmployment Type: ContractAbout The RoleWe are seeking a highly skilled Full Stack Developer with deep experience across machine learning, deep learning, knowledge graphs, and cloud-based AI platforms. You’ll help build intelligent, scalable systems that leverage advanced AI and next-generation data architectures.Key ResponsibilitiesDevelop and maintain scalable full-stack applications using Python and modern ML/DL libraries.Build, train, and deploy ML models using Vertex AI, AWS SageMaker, Azure ML, or equivalent managed ML platforms.Implement Retrieval Augmented Generation (RAG) pipelines and integrate with LLM frameworks such as LangChain or LangGraph.Design efficient algorithms for data enrichment, entity extraction, and semantic analysis.Work with knowledge graphs and graph data models, including hands-on experience with Neo4j or similar graph databases.Build metadata enrichment workflows across structured and unstructured data sources.Apply deep learning architectures and best practices to solve complex AI challenges.Design, implement, and secure A2A integrations using REST or gRPC endpoints.Lead model optimization efforts, including Model Compression and Optimization (MCP) for latency and efficiency improvements.Required Technical SkillsStrong proficiency in Python and ML/DL frameworks (TensorFlow, PyTorch, Scikit-learn).Experience with NLP tools such as spaCy, NLTK, and Hugging Face Transformers.Hands-on experience with cloud ML platforms, ideally Vertex AI.Familiarity with RAG pipelines and LLM frameworks (LangChain, LangGraph).Expertise in designing algorithms for text processing and semantic data workflows.Knowledge of knowledge graphs, data modeling, and experience with Neo4j or other graph databases.Solid understanding of deep learning methodologies and real-world deployment.Proven experience building scalable APIs and systems (REST, gRPC).Strong background in model optimization, quantization, pruning, or other MCP techniques.Preferred / Nice-to-Have SkillsExperience with AI Development Kits (ADK) and prompt engineering best practices.Knowledge of Docker, Kubernetes, and containerized model deployment workflows.Experience implementing MLOps CI/CD pipelines for ML lifecycle automation.Exposure to distributed computing frameworks (e.g., Apache Spark).Behavioral CompetenciesCollaboration: Works effectively in cross-functional and multi-disciplinary teams.Problem-Solving: Strong analytical mindset with the ability to convert ambiguity into executable tasks.Initiative: Proactive in identifying improvements, proposing technical solutions, and driving innovation.Why Join Us?Work on cutting-edge AI and ML product development.Collaborative and innovation-focused environment.Opportunities for technical growth and continuous learning.