- Architect and implement end-to-end machine learning pipelines to solve complex business problems, ensuring high model accuracy and system reliability.

- Lead the integration of advanced deep learning models into production environments using FastAPI to ensure seamless service delivery for global clients.

- Manage the full lifecycle of ML models, including versioning, monitoring, and retraining, to maintain optimal performance as data patterns evolve.

- Collaborate with cross-functional engineering teams to optimize data processing workflows, reducing latency and improving the overall efficiency of big data applications.

- Mentor junior engineers and contribute to technical decision-making processes to foster a culture of engineering excellence and innovation.

Mandatory Skills :

- Strong Python programming (Data Structures & Algorithms preferred)

- Hands-on Machine Learning using Scikit-learn, PyTorch, TensorFlow

- Classical ML : XGBoost, LightGBM, Random Forest

- Feature Engineering & Hyperparameter Tuning

- Production ML Deployment using FastAPI/Flask

- MLOps : MLflow/Kubeflow, Docker, Kubernetes

- Spark/PySpark is preferred

- Experience with Recommendation Systems, Search/Ranking, Forecasting, Fraud Detection, NLP or Computer Vision

- Strong ownership of end-to-end ML lifecycle (training, deployment, monitoring, retraining)

Preferred Background :

- Product company experience is mandatory (Meta, Google, Microsoft, Amazon, Adobe, Atlassian, Salesforce, Uber, Walmart Global Tech, PhonePe, Flipkart, etc.)

- Experience building customer-facing AI/ML products at scale

- Strong software engineering and system design skills

- Experience working closely with Product Managers and Engineering teams