Senior Machine Learning Engineer

Roles and Responsibilities

1. Good analytical skills, with expertise in analytical tool-kits such as Logistic Regression, Cluster Analysis, Factor Analysis, Multivariate Regression, Statistical modelling, predictive analysis.
2. Hands-on with Python/R programming and knowledge of machine learning tools like Scikit-Learn, Pandas, TensorFlow, Pytorch, Keras, OpenCV, XGBoost etc.
3. Computer Vision: Image preprocessing, image matching/comparison using SIFT/SURF/ORB, Object detection (Faster RCNN, YOLO, SSD, etc.), Image similarity (Siamese network), Image classification.
4. NLP: Vector Space modeling in NLP, LSTMS, Sequence modeling, Attention modeling, BERT, Transformers. Using the above mentioned techniques performing Document classification, Semantic similarity, NER, Sentiment Analysis.
5. Time Series Analysis using ARIMA, SARIMA, LSTMs, etc.
6. Knowledge of statistical modelling and popular machine learning models.
7. Feature engineering, Feature selection/Feature importance, Dimensionality
8. Reduction (PCA, etc.), Hyperparameter tuning, Ensembling techniques like Bagging, Boosting, Stacking.
9. Should possess knowledge of multiple cloud platforms (GCP-Preferable, Azure or AWS) and machine learning services offered by them.
10. Critical eye for the quality of data and strong desire to get it right.
11. A pleasantly forceful personality and charismatic communication style. Skills- Required
12. Experience in building end to end machine learning pipelines, deployment using any ML Ops Framework (Kube Flow Pipelines, Vertex AI), model performance monitoring, CI/CD.
13. Experience in GCP based ML solutions and services like Contact Center AI, Vertex AI, Document AI, Speech-to-Text, Text-to Speech, Natural Language, Dialog Flow, AutoML, Vision, Video Intelligence, Base OCR, Form Parser, Invoice Parser, etc
14. Ability to multitask and work on multiple engagements related to different domains.
15. Be able to pick up newer technologies in a short span of time.
16. Work in a highly collaborative environment by interacting with the stakeholders and various IT teams within the company to facilitate design and development of ML/AI solutions.
17. Be responsible for the successful delivery of all allocated projects with respect to schedule, quality and customer satisfaction.
18. Follow Agile standards and methodologies in all phases of the project.
19. Ensure excellence in delivery to customers.
20. Self-driven individuals with a passion for research and development and the zest for solving what matters.