Information Technology
Full-Time
Veersa Technologies
Overview
Job Description:
We're seeking a skilled and motivated ML/AI Engineer to join our team and drive end-to-end AI development for cutting-edge healthcare prediction models. As part of our AI delivery team you will be working on designing, developing, and deploying ML models to solve complex business challenges.
Key Responsibilities:
We're seeking a skilled and motivated ML/AI Engineer to join our team and drive end-to-end AI development for cutting-edge healthcare prediction models. As part of our AI delivery team you will be working on designing, developing, and deploying ML models to solve complex business challenges.
Key Responsibilities:
- Design, develop, and deploy scalable machine learning models and AI solutions.
- Collaborate with engineers and product managers to understand business requirements and translate them into technical solutions.
- Analyse and preprocess large datasets for training and testing ML models.
- Experiment with different ML algorithms and techniques to improve model performance.
- Experience:
- B.Tech with 2-5 years of relevant experience in Machine Learning, AI, or Data Science roles.
- M.Tech, M. Statistics with 2-4 years of relevant experiencein Machine Learning, AI, or Data Science roles.
- Education: Bachelor's/master's degree in computer science or Statistics or any other relevant engineering / AI course from a Tier 1 or Tier 2 college.
- Proficiency in programming languages such asPython & R
- Strong knowledge of classicalmachine learning algorithms with hands on experience in the following:
- Supervised (Classification model, regressions models)
- Unsupervised, (Clustering Algorithms, Autoencoders)
- Ensemble Models (Stacking, Bagging, Boosting techniques, Random Forest, XGBoost)
- Experience in data preprocessing, feature engineering, and handling large-scale datasets.
- Model evaluation techniques like accuracy, precision, recall, F1 score, AUC-ROC for classification, and MAE, MSE, RMSE, R-squared for regression.
- Explainable AI (XAI) techniques include methods like SHAP values, LIME, feature importance from decision trees, and partial dependence plots.
- Experience withML frameworkslikeTensorFlow, PyTorch, Scikit-learn,orKeras.
- Building & deploying Model APIs using framework like Flask, Fast API, Django, TensorFlow Serving etc.
- Knowledge ofcloud platformslike Azure (preferred), AWS, GCP and experience deploying models in such environments -> changed the wording, added points.
- Object Oriented Programming.
- Familiarity withNLP, ortime seriesanalysis. -> Moving this into Nice to have, not bare min
- Exposure todeep learningmodels (RNN, LSTM) and working with GPUs.
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