Job Description :
Job Title: Principal Software Engineer
Location : Smithfield, RI 02917
Duration : 4 Months
The Purpose of Your Role
We are undertaking big initiatives to transform our compliance, surveillance and security business partner’s ability to improve the effectiveness of firm-wide compliance surveillance processes to reduce false positives, improve detection, and to keep pace with regulatory expectations for surveillance. This we plan to achieve by bringing multiple AI models to production over the course of this year. The role’s purpose will be to help stand up the exploration, challenge and production pipelines for AI models working with data scientist, architecture and Enterprise cloud computing teams.
Responsibilities:
Design and build Docker containers to host data recipe, model, and performance analysis
Establish patterns that are repeatable for building data artifact containers, model artifact containers, and performance analysis containers in exploration, challenge and production lanes.
Use your experience in many of the following:
AWS, EKS, ECS, EMR, EC2, S3, Azure, Google, Concourse, Artifactory, GIT
Clear understanding of Elastic scaling on the cloud and the management/tuning of resources on the cloud
Kubernetes, Docker, PCF
Python scripts, objects, and modules including knowledge of creating and distribution of code built using them.
Google Tensorflow, Tensor serving, H2O.ai, and other similar frameworks
Shell scripting, Curl, Java, C++, Hadoop, Yarn, PL/SQL, Parquet files, Spark,
Knowledge of data wrangling/data recipe preparation tools such as Trifacta and model interface performance management tools such as FastCore is a plus
Use monitoring resources and to measure and recommend optimal deployment configurations
Use your experience with model life-cycle management to recommend optimal solutions for the enterprise
Qualifications:
Experience containerizing and deploying applications and services to cloud platforms such as Amazon Web Services (AWS), Azure, and Google cloud using Kubernetes and Dockers.
Current experience managing life-cycle of machine learning models deployed to the cloud using CI/CD tools such as Concourse.
Experience with AI model physical architecture and an ability to interact with architects to arrive at solutions will be key.
Prior experience supporting data scientist and building models using Python, Tensor flow, H2O.AI etc. is required.
Candidates will be required to possess the necessary organizational and leadership skills to work with multiple cross functional teams across the enterprise.
Being a highly motivated and responsive team player who can multi-task and work under minimal supervision is a must.