Machine Learning & AI Solutions
Deploy advanced machine learning models and AI-driven solutions across cloud environments.
Lead the end-to-end lifecycle of ML models, from data ingestion and feature engineering to model training, evaluation, and deployment.
Monitor model performance and implement retraining strategies to ensure long-term accuracy and relevance.
Cloud & Data Engineering
Architect and optimize scalable data pipelines and cloud-based infrastructures (Azure, AWS, or GCP) to support ML workloads.
Collaborate with data engineers to ensure robust data preprocessing, transformation, and storage strategies.
MLOps & Automation
Develop and maintain CI/CD pipelines tailored for ML workflows, including automated testing, model versioning, and deployment.
Integrate monitoring and alerting systems to track model drift, data quality, and system performance.
Collaboration & Innovation
Work closely with cross-functional teams including data scientists, product managers, and business stakeholders to translate business needs into technical solutions.
Mentor junior engineers and contribute to knowledge sharing and best practices across the team.
Requirements
Technical Skills:
- Strong proficiency in Python and ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
- Strong command of SQL and experience with large-scale data processing tools (e.g., Spark, Pandas).
- Deep understanding of cloud platforms (Azure, AWS, GCP) and their ML services.
- Skilled in MLOps practices, including CI/CD, containerization (Docker), and orchestration (Kubernetes).
- Familiarity with Git and collaborative development workflows.
Experience:
- 6+ years of experience working with complex datasets and solving real-world business problems using AI
· Proven track record of deploying and maintaining ML models in production environments.
