Main Responsibilities
- Lead the design and development of advanced machine learning and deep learning models for real-world business applications.
- Collaborate cross-functionally with data scientists, product managers, and software engineers to identify, define, and implement AI use cases.
- Drive the full model lifecycle: from data preprocessing and feature engineering to training, tuning, and production deployment.
- Architect and implement robust MLOps pipelines, including CI/CD workflows, model versioning, monitoring, and automated retraining.
- Mentor junior team members and contribute to best practices across the AI/ML function.
- Continuously optimize and scale AI systems for performance, reliability, and cost-efficiency in production environments.
- Stay ahead of the curve by exploring new research, frameworks, and tools in AI/ML, and proactively propose innovative applications.
- Contribute to architectural decisions around data and ML infrastructure.
Job Requirements
Academic Qualifications:
- Bachelor's or Master's in Computer Science, Data Science, AI, Engineering, or related fields
Experience & Technical Skills:
- 5-10 years of hands-on experience in machine learning and AI solution development, with at least 3+ years working on models in production environments.
- Strong track record of building, deploying, and maintaining ML systems at scale.
- Expert-level Python skills and deep familiarity with ML/DL frameworks such as TensorFlow, PyTorch, Scikit-learn.
- Solid grasp of machine learning algorithms, data engineering workflows, and software development best practices.
- Experience working with cloud platforms (AWS, GCP, Azure) for training, experimentation, and deployment of ML models.
- Hands-on experience with MLOps tools and practices: Docker, Kubernetes, Git, CI/CD, model monitoring.
- Strong understanding of data pipelines, including use of orchestration tools (e.g., Airflow, Kafka).
