AI/ML Engineer Full-time

Description

Total 7+ years of working experience in design and development of end to end ML pipeline with at least 5+ years of relevant experience in providing expertise in large scale ML projects
At least 5 years of hands-on data science experience in using data analytics, visualization, statistical programming and data mining for problem-solving. Domain knowledge and experience in Banking and AML is a plus.
Proven experience in analysis, design, development and implementation of end to end ML pipeline in a large scale enterprise setup.
Excellent understanding of data science, machine learning techniques, algorithms and toolkits to develop scalable solutions for production.
Applied quantitative and statistics skills, such as statistical analysis, distributions, multivariate testing, regression, classification and optimization algorithms etc. to design tests, interpret and explain results
Familiar with MLOps tools like MLflow or Kubeflow, deep learning frameworks like Tensorflow or Pytorch
Familiar with CI/CD tools for deploying ML models
Knowledge in big data systems (Hadoop, Spark, Hive)
Working experience in Dataiku is an advantage
Support the team providing end-to-end solution, starting from understanding the requirements, creating research proposals, implementation of the research by exploring different methods and algorithms, documenting and presenting results.
Use analytical and statistical methods, programming and data modeling to analyze large amounts of data and come up with actionable insights
Generate and test hypotheses, designing experiments to answer targeted questions of advanced complexity
Documents projects including business objectives, data gathering and processes, leading approaches, final algorithm, and detailed set of results and analytical metrics
Work with Product Business Analysts, data scientists, ML engineers, business stakeholders and other technology teams to ensure quality solution is delivered at enterprise scale
Provide thought leadership by researching standard methodologies in AI/ML, collaborating and contribute actively on the standards and best practices
Contribute to the team in various aspects – knowledge sharing, guidance to more junior members, contribute to shared code library, establish best practices and methodologies.
Take ownership and have end to end responsibility from planning stage to presenting results
Liaise with Global stakeholders and effectively communicate to be influential within Global AML Landscape.