Fidelity

Job Description:

Position Description:

Builds and maintains large scale Machine Learning (ML) Infrastructure and ML pipelines, dashboards, platforms and tools to enable both prediction and optimization of model development. Performs advanced analytics and scales existing ML platforms and frameworks for model trains and deployment. Develops ML operations using various cloud technologies and collaborates with Data Scientists to robustly scale up ML Models for large volumes in production, to deliver personalized experiences to customers. Partners with Applied Data Scientists to build models and to simplify data and ML ecosystem. Analyzes information to determine, recommend, and plan computer software specifications on major projects and proposes modifications and improvements based on user need. Provides business solutions by developing complex or multiple software applications.

Primary Responsibilities:

• Develops original and creative technical solutions to on-going development efforts.
• Designs applications or subsystems on major projects and for/in multiple platforms.
• Develops applications for multiple projects supporting several divisional initiatives.
• Supports and performs all phases of testing leading to implementation.
• Assists in the planning and conducting of user acceptance testing.
• Develops comprehensive documentation for multiple applications supporting several corporate initiatives.
• Responsible for post-installation testing of any problems.
• Establishes project plans for projects of moderate scope.
• Works on complex assignments and often multiple phases of a project.
• Performs independent and complex technical and functional analysis for multiple projects supporting several initiatives.

Education and Experience:

Bachelor’s degree (or foreign education equivalent) in Computer Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and three (3) years of experience as a Senior Software Engineer/Developer (or closely related occupation) building, deploying, and maintaining scalable ML infrastructure in Cloud Amazon Web Services (AWS).

Or, alternatively, Master’s degree (or foreign education equivalent) in Computer Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and one (1) year of experience as a Senior Software Engineer/Developer (or closely related occupation) building, deploying, and maintaining scalable ML infrastructure in Cloud Amazon Web Services (AWS).

Skills and Knowledge:

Candidate must also possess:

• Demonstrated Expertise (“DE”) architecting, developing, and deploying multi-tier, in-house patterns in Application Containerization Infrastructure Deployment (ACID) framework and Software Development Life Cycle (SDLC) framework using Agile to deliver at scale; and improving application performance and supporting business rules to ensure observability and resiliency while writing production-level code adhering to PEP 8 standards.
• DE developing and migrating web services using SOAP, REST API, Swagger API, and API Gateway; integrating third-party vendor services and implementing Continuous Integration (CI) and Continuous Delivery (CD) pipelines into Amazon Web Services (AWS) and Azure Cloud Services; building, deploying, and maintaining Machine Learning (ML) and data solutions utilizing Jenkins, Docker, and uDeploy; and developing Application Programming Interfaces (APIs) using Postman, Insomnia, Jenkins, and Docker.
• DE analyzing and evaluating ML and Deep Learning (DL) models on text data using various Natural Language Processing (NLP) using Term Frequency-Inverse Document Frequency (TF-IDF) and Named Entity Recognition (NER); reporting suspicious activity using logistic regression and decision trees, and providing recommendations using Linear Regression and Keras; implementing Time Series models in real time prices and confidence intervals using LSTMs, Regression, ARIMA, SARIMAX, and Python libraries; and generating interactive analytics dashboard, reporting, and visualizations using Power BI, Tableau, or Splunk.
• DE architecting, developing, and deploying cloud infrastructure using Azure Cloud Services (Azure ML, Azure Function Apps, or Azure Data Lake) and AWS Cloud Services (AWS SageMaker, AWS Lambda, AWS State Machine, AWS CloudFormation, and AWS S3); enhancing ETL automation and data processing capabilities using AWS Lambda, Batch, or Azure Function; and migrating applications to cloud-enabled APIs using AWS, Azure services, and data bricks services, ensuring scalability and performance optimization.

#PE1M2

#LI-DNI

Certifications:

Category:

Information Technology

Fidelity’s hybrid working model blends the best of both onsite and offsite work experiences. Working onsite is important for our business strategy and our culture. We also value the benefits that working offsite offers associates. Most hybrid roles require associates to work onsite every other week (all business days, M-F) in a Fidelity office.

Please be advised that Fidelity’s business is governed by the provisions of the Securities Exchange Act of 1934, the Investment Advisers Act of 1940, the Investment Company Act of 1940, ERISA, numerous state laws governing securities, investment and retirement-related financial activities and the rules and regulations of numerous self-regulatory organizations, including FINRA, among others. Those laws and regulations may restrict Fidelity from hiring and/or associating with individuals with certain Criminal Histories.