Odiin.AI
You will work closely with data, product, and engineering teams to develop scalable AI systems and integrate models into production environments.
Key Responsibilities Design, develop, and deploy machine learning and AI models for production use Build and maintain scalable data pipelines and model-serving infrastructure Train, evaluate, and optimise models for performance, accuracy, and efficiency Work with large datasets to extract insights and improve model outcomes Collaborate with product and engineering teams to translate business needs into AI solutions Monitor, test, and iterate on models in live environments Ensure best practices around model performance, reliability, and security
Required Skills & Experience Strong experience in Python and common ML libraries (TensorFlow, PyTorch, scikit-learn) Solid understanding of machine learning fundamentals and algorithms Experience deploying models to production environments (cloud or on-prem) Familiarity with data processing tools and pipelines Understanding of model evaluation, optimisation, and monitoring techniques
Nice to Have Experience with large language models (LLMs) or generative AI Knowledge of MLOps tools and workflows Experience working with cloud platforms (AWS, GCP, Azure) Background in applied research or experimentation
Key Responsibilities Design, develop, and deploy machine learning and AI models for production use Build and maintain scalable data pipelines and model-serving infrastructure Train, evaluate, and optimise models for performance, accuracy, and efficiency Work with large datasets to extract insights and improve model outcomes Collaborate with product and engineering teams to translate business needs into AI solutions Monitor, test, and iterate on models in live environments Ensure best practices around model performance, reliability, and security
Required Skills & Experience Strong experience in Python and common ML libraries (TensorFlow, PyTorch, scikit-learn) Solid understanding of machine learning fundamentals and algorithms Experience deploying models to production environments (cloud or on-prem) Familiarity with data processing tools and pipelines Understanding of model evaluation, optimisation, and monitoring techniques
Nice to Have Experience with large language models (LLMs) or generative AI Knowledge of MLOps tools and workflows Experience working with cloud platforms (AWS, GCP, Azure) Background in applied research or experimentation