Implementing complex real-time data and AI/ML applications to capture and codify knowledge and automate decisions. Developing ML/AI models for business teams along with metrics to track their accuracy and performance; Overseeing the full lifecycle of algorithm development from ideation and data exploration, algorithm design and testing, algorithm development and deployment, to algorithm monitoring and tuning in production. Evaluating ML algorithms that could be used to solve a given problem and ranking them by their success probability; and Acting as an internal resource for AI/ML specific needs, providing guidance, analysis, and context to internal stakeholders during meetings and other strategy activities. Must have experience with the following in order to complete the above-mentioned tasks: Required experiences and skillsets: 1. Machine Learning; Familiar with MLFlow, Kedro, Pyspark, Pytorch frameworks (Over 2 years) Expertise of using commonly used ML/AI Models/libraries such as Pandas, Numpy, Tensorflow2/3, Keras, BERT, LayoutLMV1/2/3 as well as regressions, decision trees, random forest, K-means, etc traditional ML algorithms. (Over 2 years) Expertise of distributed ML/AI training libraries/models: Koalas, Horovod, DDP. 2. Python programing and Software Engineering Expertise of Pythonic clean coding, developing decorators, generators and descriptors for Python modules/libraries (over 2 years) Expertise of Software Design by Contract, particular over X years of DRY/OAOO, YAGNI, KIS, EAFP/LBYL, Defensive programming Expertise of separating cohension and coupling concerns in software design. (Over 2 years) Expertise of structing the code and orthogonality in software. (Over 2 years) Expertise of the SOLID Principles (Single responsibility, Open/closed, Liskov's substitution, Interface segregation and Dependency Inversion) (Over 2 years) 3.Cloud DevSecOps Over 1 years of coding container image dockfile and K8S Over 1 years of coding YAML scripts Expertise of building CI/CD pipelines, particular of programming in Jenkins, Github, Nexus, Sonar, Unit test framework, Pytest. Over 2 years of Cloud, familiar with at least 1 of Azure, GCP and AWS. Familiar with node affinity, container, pods, namespace and cluster (over 2 years) Better have experience of protecting data in production using Key-vaults, cloud HSM, KMS. Requires minimal Bachelor's degree or foreign equivalent in Computer Science, Electrical Engineering, or a closely-related field, and at least 4 years of experience as an software engineer and 3 years years of ML related programing as well as at least 1 year of Cloud DevSecOps experience or related occupation. Qualifications .