Data Science Engineer (Artificial Intelligence)

A) Overall:

• Experience in devising/enhancing machine learned (DL, NN) algorithms and data mining techniques for solving complex Big Data and enabling key business use cases through completion must have delivered valuable Insights. Candidate should have lead the AI-based engagement through completion including collaborating with client on value messaging, use case elaborations and managing AI Engagement lifecycle (through deployment) while managing a team.

• Excellent client facing skills communication, facilitation and collaboration.

• Overall experience of 5+ years specific AI experience of minimum of 4+ years.

B) Machine Learning (Must have):

• Experience implementing ML algorithms such as Logistic Regression, Naive Bayes, Bayesian Network, Decision Tree, Neural Network, SVM, Boosting, K-Means, Ensembler Classifiers, Random Forest, convex optimization, transfer learning.

• Experience in frameworks such as Spark, Lucene or EMR to implement real-time ML systems

C) Data / AI Engineering (Must have):

• Proven experience working with large scale data foundations data acquisition, processing / transformation, feature engineering and building production quality data foundation.

• Hands-on expertise in scripting Python, R.

D) AI / DL Frameworks

• Knowledge of atleast two frameworks AI / DL frameworks Tensorflow, Keras, MxNet, Caffe. CNTK, Theano / Pytorch

• Hands-on usage of leading tools for productionizing DL solutions AWS Sagemaker / Libraries (like Rekognition), Azure ML components (Studio etc.), H2O AI.

• Hands-on scripting using the frameworks Python or R.

E) Should have deep expertise in atleast one of the following:

Computer Vision, Speech Recognition and / or NLP OCR

Strong understanding of Machine Learning techniques especially deep learning
Good Programming skills in C, Python Shell scripting is desirable.