You will lead a team of ML engineers, collaborate with cross-functional teams, and ensure the scalability, efficiency, and reliability of our ML solutions.

This role requires strong technical expertise, leadership, and a deep understanding of ML systems in production.

Key Responsibilities :

- End-to-End ML Development Design, build, and deploy scalable ML models for real-world applications.

- Team Leadership Guide and mentor a team of ML engineers and data scientists.

- Model Optimization & MLOps Implement model monitoring, retraining, and CI/CD pipelines for ML models.

- Big Data & Cloud Integration Work with large-scale data pipelines, distributed computing, and cloud services (AWS/GCP/Azure).

- Cross-Functional Collaboration Work closely with data engineers, product teams, and software developers.

- Algorithm Innovation Research and implement state-of-the-art ML and deep learning techniques.

- Performance & Scalability Ensure ML models are efficient and optimized for deployment in production environments.

Requirements :

- Education : Bachelor's or masters in computer science, AI, Data Science, or a related field.

- ML Expertise : Strong background in Supervised, Unsupervised, and Deep Learning techniques.

- Tech Stack : Python, TensorFlow, PyTorch, Scikit-learn, Spark, Kubernetes, Docker.

- Data & Infrastructure : Experience with Big Data (Hadoop, Snowflake), Cloud ML tools (SageMaker, Vertex AI).

- MLOps & CI/CD : Hands-on with ML lifecycle management, monitoring, and automation.

- APIs & Deployment : Experience in deploying ML models via REST APIs and microservices