AI Engineer

Functional/ TechnicalDesign, develop and deploy machine learning solutions and servicesImplement end-to-end machine learning pipelines from data ingestion to training and model servingOperationalize LLMs, embeddings, and multi-agent systems in real-world applicationsManage the machine learning and model lifecycle (experimentation, registry, deployment)Oversee the model promotion lifecycle, coordinating validation gates and approval workflows to safely deploy new model versions from stating to productionContainerize applications using Docker and orchestrate them via KubernetesBuild and maintain CI/CD pipelines for ML models and LLM applicationsCollaborate with data scientists to refactor research code into production-ready Python codeMonitor model performance, data drift, and performance in productionAssess and integrate AI solutions ensuring optimal performance and reliabilityDesign and implement production grade RAG systemsCollaborate with infrastructure teams, data engineers, data scientists, and other stakeholders to integrate machine learning solutions into existing systems and processesParticipate in code reviews, testing, and debugging to ensure the quality and reliability of machine learning solutionsCompetenciesStrong problem-solving and analytical skills, with the ability to think critically and creatively about complex challengesExcellent communication and collaboration skills, with the ability to work effectively with cross-functional teams and stakeholders at all levels of the organizationAbility to manage personal workloads effectively, to prioritize tasks, manage timelines, and deliver high-quality results on scheduleContinuous learning mindset, with a passion for staying up to date with the latest advancements in machine learning and artificial intelligenceAttention to detail and commitment to producing high-quality, reliable, and maintainable codeEducation and skills requirementsBachelor's or Master's degree in Data Science, Computer Science, Mathematics, Statistics, or a related fieldAdvanced proficiency in Python programming with a focus on writing clean, testable and efficient codeDevOps & Containers: Proficient with Docker for containerization and working knowledge of Kubernetes (k8s) for orchestrationPractical understanding of GPU architecture and cloud compute instances to optimize resource allocation for training and inference workloadsMLOPS tools: hands on experience with MLflow (or similar tools like weights & biases) for experiment tracking and model registryProven experience working with Large Language Models (LLMs)Good understanding of AI agents & agentic workflows, LLM orchestration frameworks and reasoning patternsExperience with data preprocessing, feature engineering, and model selection and evaluation techniquesHands-on experience with CI/CD pipelines (GitLab, Jenkins)Knowledge of statistical and mathematical concepts relevant to machine learning, such as probability, linear algebra, and optimizationExcellent problem-solving and debugging skills, with the ability to identify and resolve issues quickly and effectivelyRelevant work experience in machine learning, data science or a related field