Senior Software Engineer - ML Platform & Cloud Infra

Fidelity Investments Inc.; Durham (NC); On-site; USD 120,000 - 190,000; Full time

Job description

Designs software and implements Object Oriented Programming (OOP) using Java, Scala, and Python. Writes scripts using Unix. Develops Machine Learning (ML) infrastructure and ML Operations in the Cloud using Amazon Web Services (AWS). Builds and maintains large scale ML infrastructure and pipelines. Contributes to building advanced analytics, ML platforms, and tools to enable prediction and optimization of models. Provides business solutions by developing complex or multiple software applications to support Data Science-oriented frameworks and business solutions.

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.

• Develops scalable, modular, and safe technical solutions to support ML projects.

• 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) developing ML platform applications for Cloud infrastructures (Amazon Web Services (AWS), Azure, Google, and IBM) using Agile Methodologies.

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) developing ML platform applications for Cloud infrastructures (Amazon Web Services (AWS), Azure, Google, and IBM) using Agile Methodologies.

Skills and Knowledge:

Candidate must also possess:

• Demonstrated Expertise (“DE”) designing, building, and applying auto-ML infrastructure and tools to develop, deploy, monitor, and interpret ML models using AWS SageMaker, Google Colab, Azure Cloud, and Anaconda; and building tools to detect data drifts that impact prediction quality and model monitoring, interpretability, and explainability using Python’s ML and Deep Learning (DL) ecosystem (numpy, panda, sklearn, tensorflow, and keras).

• DE designing and developing scalable and secure applications using Cloud technologies according to standard security practices and distributed architectural requirements – Identity and Access Management (IAM), fine-grain access controls, and encryption schemes.

• DE achieving auto-scale analysis for users and use cases within an organization environment using Kubernetes and Open-Source Software (Jupyter and Kubeflow); accelerating data exploration and model development using notebook interfaces and tools (Dask Spark Technology); applying version control (Git) while developing AI solutions in Agile methodologies; and deploying data science infrastructure using CI/CD and orchestration tools (AWS Step Functions, Airflow, and Kubeflow).

• DE analyzing Big Data applications; designing and developing batch processing jobs that perform ETL to support predictive analytics using Hadoop, MongoDB on Cloudera, Google Cloud, or AWS and navigation tools (analytical functions offered by PostgreSQL); building and troubleshooting high performance big data applications using multi-threading, multi-processing, asynchronous programming, MapReduce techniques, and programming languages (Python and Java).