Machine Learning Engineer

Key ResponsibilitiesArchitect and Develop AI/ML Pipelines:Design and implement large-scale, production-grade AI/ML pipelines using appropriate tools and algorithms.Lead the full end-to-end ML lifecycle: model development, deployment, monitoring, and optimization for both on-prem and cloud environments.Data Solution Design and Management:Define, develop, and sustain data solutions including storage, processing units, and user interfaces for industrial-grade applications.Build multi-tenant data collectors and storage systems.Implement streaming and batch data processing for near-real-time flows.Data Modeling and Quality:Design scalable data models (SQL and NoSQL).Assess and improve data quality across incoming data flows.Apply data management and security best practices.Analytics and Visualization:Develop customizable analytical dashboards for stakeholders.Ensure strong testing and quality assurance practices throughout development.Innovation and Technology Evaluation:Stay updated on emerging technologies and evaluate opportunities for integration.Contribute to continuous improvement of AI/ML solutions and processes.QualificationsEducation: Master’s or Ph.D. in Computer Science, Data Engineering, Machine Learning, or related field.Experience:Proven experience in designing and deploying AI/ML solutions at scale.Strong background in data engineering and pipeline development.Hands-on experience with cloud platforms (AWS, Azure, GCP) and on-prem solutions.Technical Skills:Proficiency in Python, SQL, and NoSQL databases.Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).Knowledge of data streaming tools (Kafka, Spark) and containerization (Docker, Kubernetes).Experience working with Microsoft solutions and Databricks is a plusFamiliarity with CI/CD and MLOps practices.Soft Skills:Strong problem-solving and analytical mindset.Ability to work collaboratively in cross-functional teams.Excellent communication and documentation skills.