Architect 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.

Education: 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 plus
- Familiarity 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.

Machine Learning Engineer