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Sr. Associate Machine Learning Engineer

5.00 to 7.00 Years   Bangalore   09 Oct, 2021
JPMorgan
Job LocationBangalore
EducationNot Mentioned
SalaryNot Disclosed
IndustryBanking / Financial Services
Functional AreaGeneral / Other Software,Sales / BD
EmploymentTypeFull-time

Job Description

We are looking for a Machine Learning Ops Engineer to join our AIML team to solve exciting business problems in the domain of commercial banking, payments and financial services. Candidates must have a strong engineering and DevOps background and passionate about applying engineering methodology and best practice to machine learning development life cycle. This role will work with Data Scientist engineers on the team very closely to build model pipeline from data processing, to model development/testing then finally bring the model to production environment. This is a unique opportunity to apply your skills and have a direct impact on global business.The ideal candidate will have strong software engineering skills and ability to build systems that reach JP Morgan scale. Knowledge of ML and have experience working with massive amounts of data is a big plus. What Youll Do:

  • Work with data scientist closely to build and train production grade ML models on large-scale datasets to solve various business use cases for Commercial Banking.
  • Design a reusable model life cycle pipeline to make model development and testing a repeatable process.
  • Deploy and maintain models in production environment by providing versioning control of model and hyperparameters, and provide interface to troubleshoot issues in production environment; Able to retrain and redeploy the model without interrupt business applications.
  • Provide centralized dashboard to monitor CB AIML cloud usage in both dev and production environments to avoid unnecessary cost; monitor model performance in production environment and trigger re-training process if needed.
  • Build, test, and improve/maintain ETL data pipelines.
  • Simplify model development by providing automated tools and minimize manual work from data scientist engineers.
  • Collaborate and communicate with other stakeholders including data service and ML platform teams on regular basis.
  • Standardize and formalize MDLC process in CB and possibly introduce to other LOBs.
  • Help with audit process, and make sure CB modeling and data usage comply with regulations
Basic Qualifications:
  • Bachelor or Master s degree in Computer Science, Information Technology, or equivalent technical field.
  • Minimum 5+ years of working experience as a software developer, DevOps or relevant engineering experience.
  • Fluent in at least one programming language e.g. Python, Java, C or C++
  • Fluent in query languages like SQL, Cypher, HiveQL etc.
  • Past experience in working in Big Data engineering ecosystem (i.e. Hadoop/DataLake, spark, ETL pipeline) is desired
  • Experience with Enterprise Cloud infrastructure (AWS, Azure, GCP) in a mission critical environment
  • Hands-on experience with cloud-based technologies and tools especially in deployment, monitoring and operations, such as Data Dog, Prometheus, Splunk, Elasticsearch, Grafana
  • Strong working knowledge of modern development technologies and tools such as Agile, CI/CD, Git, Terraform and Jenkins.
  • Experience working with end-to-end pipelines using frameworks like KubeFlow, TensorFlow, Apache Airflow.
  • Excellent communication and team work
Preferred Skills/Experience:
  • Familiar with any of the AI/machine learning frameworks, statistical packages, and libraries: Tensorflow, Amazon Machine Learning, Apache Spark, PyTorch, Scikit-learn etc.
  • Familiar with machine learning techniques and advanced analytics (e.g. regression, classification, clustering, time series, econometrics, causal inference, mathematical optimization) is a plus.
  • Prior experience of designing, developing and maintaining Machine Learning solution through its Life Cycle is highly advantageous
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Keyskills :
life cycleuse casesmodel developmentmachine learningdata processingbig datamission criticaltime seriesapache sparkcausal inferencedata engineeringcomputer science

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