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).
