What you'll do:

• Ability to multitask and work on multiple engagements related to different domains
• Work in a highly collaborative and fast paced environment by interacting with the stakeholders and various IT teams within the company to facilitate the design and development of ML/AI solutions
• Responsible for the successful delivery of all allocated projects with respect to schedule, quality, and customer satisfaction
• Work with the pre-sales team on RFP, RFIs and help them solutioning for different AI/ML use cases
• Evaluate latest technologies, decide technical feasibility, and drive solution implementations
• Follow Agile standards and methodologies in all phases of the project and ensure excellence in delivery to customers
• Refine coding standards, software development guidelines, and best practices within the organization, and ensure adherence to those
• Mentor juniors within the organization, define and track their growth parameters

What makes you a great fit:

• Strong interpersonal and written skills with clear and precise communication
• Experience working in an Agile and competitive environment
• Stakeholder interaction experience both within the organization and outside with clients
• Strong analytical and quantitative skill set with proven experience solving business problems across domains
• Very good with EDA, Hypothesis Testing, Feature Engineering
• Hands-on with Python/R programming and ML/Viz. libraries/frameworks like Scikit-Learn, Pandas, Matplotlib, Seaborn, D3.js, Tensorflow, Pytorch, Keras.
• Experience with ML algorithms such as Regression and Classification (Decision-trees, Random Forests, SVM, ANNs), Clustering (k-means, DBSCAN), Dimension Reduction (PCA, SVD), Ensemble techniques (XGBoost, CatBoost, LightGBM)
• Basic image enhancement techniques such contrast enhancement, blurring, histogram equalization, etc using OpenCV
• Experience with DL/CV techniques like CNNs, Faster RCNN, Mask RCNN, YOLO, SSD, Detectron2 for various use cases related to image such as Image Classification, Object Detection, Image Segmentation, etc
• Traditional NLP - Bag of words, tf-idf, Stemming, Lemmatization, Tokenization, POS tagging, Coreference Resolution, Dependency and Constituency Parsing, Named Entity Recognition
• NLP: NLU vs NLG, Vector Space modeling and text representation techniques in NLP, Knowledge/experience using RNNs,
• LSTMS, Sequence modeling and Attention mechanism, Transformers, BERT, GPT and their SOTA variants, Sequence modeling, Attention modeling, BERT, Transformers.Using the above-mentioned techniques for Text classification, Sentiment Analysis, Semantic similarity, Entity Extraction, Document summarization, NLI, Question-Answering, Machine Translation, etc
• Forecasting modeling experience both on univariate and multivariate data using algorithms like Linear Regression, Neural Networks, Exponential Smoothing, Holt’s Winters, ARIMA, SARIMA, LSTM
• Identify appropriate objective functions, regularization techniques, performance metrics based on the use case and should be able to perform cross-validation, hyperparameter tuning, and error analysis