Senior Applied AI Engineer

Accountabilities: Own the end-to-end development and delivery of production-grade AI and machine learning systems, from research and experimentation to deployment and monitoring. Train, fine-tune, optimize, and maintain machine learning models, including large language models and open-weight AI models. Build and manage scalable data processing, training, inference, and evaluation pipelines to support production AI workloads. Improve model performance across key metrics such as accuracy, latency, reliability, scalability, and cost efficiency. Implement MLOps best practices, including CI/CD pipelines, automated retraining processes, model monitoring, and governance frameworks. Develop evaluation methodologies, benchmark datasets, and quality assurance mechanisms to ensure model robustness and performance. Design and maintain scalable APIs and backend services that expose AI capabilities to internal and customer-facing applications. Collaborate closely with product, frontend, backend, and infrastructure teams to integrate AI solutions into enterprise workflows. Monitor production environments, troubleshoot issues, and continuously enhance both model and infrastructure performance. Research and evaluate emerging AI techniques, tools, and frameworks to drive innovation and improve product capabilities. Requirements Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Machine Learning, or a related field. Minimum of 3 years of experience as an AI Engineer, Machine Learning Engineer, Applied AI Engineer, or similar role. Strong experience training, fine-tuning, deploying, and maintaining machine learning models in production environments. Advanced proficiency in Python and hands-on expertise with machine learning frameworks such as PyTorch, TensorFlow, and scikit-learn. Experience managing production ML systems and building scalable AI architectures. Hands-on experience with cloud platforms such as AWS, Google Cloud Platform, and/or Microsoft Azure. Familiarity with managed machine learning services including Amazon SageMaker, Vertex AI, or similar platforms. Strong understanding of API design principles, distributed systems, and scalable backend architectures. Practical experience implementing MLOps practices, including CI/CD pipelines, model monitoring, observability, and automated deployment workflows. Experience working with Docker, Kubernetes, PostgreSQL, and modern data infrastructure technologies. Strong knowledge of large language models, retrieval-augmented generation (RAG) architectures, embeddings, and vector databases is highly desirable. Excellent analytical thinking, problem-solving abilities, and communication skills, with strong written and verbal English proficiency. Benefits Fully remote opportunity offering flexibility and strong work-life balance. Opportunity to work on cutting-edge AI technologies, including LLMs, generative AI, and enterprise-scale machine learning systems. Exposure to modern cloud-native architectures, MLOps frameworks, and advanced AI infrastructure. Collaborative and high-growth environment with significant ownership and autonomy. Opportunity to directly influence product innovation and AI strategy through impactful projects. Continuous learning and professional development opportunities in a rapidly evolving field. Fast-paced startup culture that encourages experimentation, creativity, and rapid career growth. Structured recruitment process with direct exposure to technical leadership and founders.