AI Architect

Education and Work Experience Requirements: 10+ years of experience working with large data sets or do large scale quantitative analysis Will be responsible for leading architecture, design and implementation of AI/ML / NLP / Computer Vision and MLOps solutions across team of Data Scientists, Data Engineers, ML Engineers, Cloud, Analytics, DevOps and Visualization teams Experience in analyzing complex problems and translating them to data science algorithms with due attention to computational efficiency and testing at scale. Authoritative experience in the field of Data Science to influence stake holders across varied work streams and geographical locations, ability to challenge the status quo to improve the base line models and the models which are already productionized. Mandatory Skills: Experience in machine learning, supervised and unsupervised: Forecasting, Classification, Data/Text Mining, NLP, Search Algorithms, Deep Learning Algorithms. Experience in using MLOps frameworks like Kubeflow, MLFlow, Airflow Pipelines for building, deploying, and managing multi-step ML workflows based on Docker containers and Kubernetes. Experience with workflow orchestration tools like Kubeflow, Airflow, Argo or similar tools Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc. ) Experience in frameworks to depict interpretability of models using libraries like Lime, Shap etc. Experience working with big data - identifying trends, patterns, and outliers in large volumes of data. Strong implementation experience with Python, and familiarity with Linux/Unix/Shell environments Strong hands-on skills in sourcing, cleaning, manipulating and analyzing large volumes of data using distributed computing platform Experience with Hive / Spark / Teradata and NOSQL databases Experience in using code versioning tools like GIT, bit bucket; building CI/CD pipelines for ML projects Experience in productionizing ML models in any of the cloud platforms - AWS/GCP/Azure Good to have experience in building automated/semi-automated model retraining pipelines. Superior verbal, visual and written communication skills to educate and work with cross functional teams on controlled experiments. Candidates with prior publications in the field of Data Science are highly preferred. Tech savvy and willing to work with open-Source Tools Willingness to learn IoT, Edge AI and keep up to date with technologies Good to have previous whitepaper submissions and IP filings Good to have skills : Experience with Mobility / Automotive / Manufacturing domainWhitepaper submissions and IP filing