10 - 15 years
25 - 35 Lacs
Posted:None|
Platform:
Hybrid
Full Time
Employees in this job function are responsible for designing, building, deploying and scaling complex self-running ML solutions in areas like computer vision, perception, localization etc. They also automate and optimize the end-to-end ML model lifecycle using their expertise in experimental methodologies, statistics, and coding for tool building and analysis.
1) Collaborate with business and technology stakeholders to understand current and future ML requirements 2) Design and develop innovative ML models and software algorithms to solve complex business problems in both structured and unstructured environments 3) Design, build, maintain and optimize scalable ML pipelines, architecture and infrastructure 4) Use machine language and statistical modeling techniques such as decision trees, logistic regression, Bayesian analysis and others to develop and evaluate algorithms to improve product/system performance, quality, data management and accuracy 5) Adapt machine learning to areas such as virtual reality, augmented reality, object detection, tracking, classification, terrain mapping, and others. 6) Train and re-train ML models and systems as required 7) Deploy ML models and algorithms into production and run simulations for algorithm development and test various scenarios 8) Automate model deployment, training and re-training, leveraging principles of agile methodology, CI/CD/CT (Continuous Integration/ Continuous Deployment/ Continuous Training) and MLOps 9) Enable model management for model versioning and traceability to ensure modularity and symmetry across environments and models for ML systems
Python, Machine Learning, AI/ML, GCP, Docker
V2soft
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