Artificial Intelligence/Machine Learning Engineer
4 weeks ago
We are seeking a highly skilled AI/ML Engineer to join our team at Gloify. The ideal candidate will have a passion for applying artificial intelligence, machine learning, and data science techniques to real-world problems.
Key Responsibilities:- Design, develop, and deploy machine learning models and algorithms for production environments, ensuring scalability and robustness.
- Leverage graph databases, particularly Neo4j, to model, query, and analyze data relationships in large-scale, connected data systems.
- Build and optimize ML pipelines, ensuring they are production-ready and can handle high volumes of data in real-time.
- Develop rule-based systems and work with complex logical inference engines to handle business logic, constraints, and decision-making processes.
- Collaborate with data scientists, software engineers, and product teams to integrate ML solutions into existing products and platforms.
- Implement algorithms for entity resolution, recommendation engines, fraud detection, or other graph-related tasks, using graph-based ML techniques.
- Work with large datasets, performing exploratory data analysis, feature engineering, and model evaluation.
- Monitor, test, and iterate on ML models post-deployment to ensure continuous improvement in model performance and adaptability.
- Participate in architectural decisions, ensuring the efficient use of graph databases and ML models.
- Stay up-to-date with the latest advancements in AI/ML research, particularly in graph-based machine learning, reasoning systems, and logical AI.
- Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
- 2+ years of experience in AI/ML engineering or a related field, with hands-on experience in building and deploying ML models in production environments and self-made projects using graph databases.
- Proficiency in Neo4j or other graph databases, with a deep understanding of Cypher query language and graph theory concepts.
- Strong programming skills in Python, Java, or Scala, along with experience using ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
- Experience with machine learning pipelines and tools like Airflow, Kubeflow, or MLflow for model tracking and deployment.
- Hands-on experience with rule engines or logic programming systems (e.g., Drools, Prolog).
- Experience with cloud platforms such as AWS, GCP, or Azure for ML deployments.
- Familiarity with containerization and orchestration technologies like Docker and Kubernetes.
- Experience working with large datasets, SQL/NoSQL databases, and handling data preprocessing at scale.
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