Job roles and responsibilities:
- 4 to 5 Years Design, develop, test, deploy, maintain and improve ML models/infrastructure and software that uses these models
- Experience writing software in one or more languages such as Python, Scala, R, or similar with strong competencies in data structures, algorithms, and software design
- Experience working with recommendation engines, data pipelines, or distributed machine learning
- Experience working with deep learning frameworks (such as TensorFlow, Keras, Torch, Caffe, Theano)
- Knowledge of data analytics concepts, including bigdata, data warehouse technical architectures, ETL and reporting/analytic tools and environments
- Participate in cutting edge research in artificial intelligence and machine learning applications
- Contribute to engineering efforts from planning and organization to execution and delivery to solve complex, real world engineering problems
- Working knowledge on different Algorithms and Machine Learning techniques like, Linear & Logistic Regression analysis, Segmentation, Decisions trees, Cluster analysis and factor analysis, Time Series Analysis, K-Nearest Neighbour, K-Means algorithm, Random Forests Algorithm, NLP (Natural language processing), Sentimental analysis, various Artificial Neural Networks, Convolution Neural Nets (CNN), Bidirectional Recurrent Neural Networks (BRNN)
- Demonstrated excellent communication, presentation, and problem-solving skills
Technical Skills Required:
- GCP Native AI/ML services like Vision, NLP, Document AI, Dialogflow, CCAI, BQ etc.,
- Proficiency with a deep learning framework such as TensorFlow or Keras, etc.,
- Proficiency with Python and basic libraries for machine learning such as scikit-learn and pandas, jupyter notebook
- Expertise in visualizing and manipulating big datasets
- Ability to select hardware to run an ML model with the required latency
- Good to have MLOps and Kubeflow knowledge
- GCP ML Engineer Certification would be a PLUS
