Senior AI/ML Engineer

Job Description:

Position Description: 

Builds and maintains large scale Machine Learning (ML) Infrastructure and ML pipelines using Big Data ML toolkits -- SparkML and Amazon SageMaker. Designs, develops, and deploys multi-tier internet facing Web and mobile applications, using Cloud ML Application Programming Interfaces (APIs). Builds and scales ML operations using Cloud technologies -- Docker, Kubernetes, Amazon Web Services (AWS), AWS Command Line Interface (CLI), and AWS CloudFormation. Builds dashboards and ML platform tools to enable the prediction and optimization of model development, using SCRUM and Kanban Agile methodologies. Adheres to standard codeing methodologies and modern Continuous Integration/Continuous Delivery (CI/CD) pipelines, using Jenkins and Version Control (Git). Writes code with Object-Oriented (OO)/object function scripting languages (Python, Java, C++, and Scala).   

Primary Responsibilities: 

• Delivers high quality data solutions in a multi-developer environment. 

• Analyzes information to determine, recommend, and plan computer software specifications on major projects. 

• Proposes modifications and improvements based on user need.  

• Develops software system tests and validation procedures, programs, and documentation. 

• Collaborates with upper management to identify and solve problems, and to clarify management objectives.  

• Presents the results of mathematical models and data analysis to management and other end users. 

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 AI/ML Engineer (or closely related occupation) building, deploying, and maintaining scalable ML infrastructure in the 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 AI/ML Engineer (or closely related occupation) building, deploying, and maintaining scalable ML infrastructure in the Cloud (Amazon Web Services (AWS)). 

Skills and Knowledge: 

Candidate must also possess: 

• Demonstrated Expertise (“DE”) analyzing and evaluating ML or Deep Learning (DL) models on text and image data, using TF-IDF, NER Natural Language Processing techniques, and recommendation (Linear Regression and Keras) and Generative capabilities (BERT-like models); writing production-level code using PEP 8 coding standards and ML model compression techniques -- model quantization and pruning; and building multi-task model training pipelines, and deploying and maintaining ML solutions, using pipelines and Docker containers (for model orchestration and monitoring). 

• DE developing Web services (producers and consumers) according to microservices architecture, using SOAP/REST API and Swagger API; implementing Continuous Integration (CI) and Continuous Delivery (CD) into AWS Cloud services (Sagemaker, API Gateway, ECS, Serverless Framework/Lambda, and S3) using Jenkins, Docker, Kubernetes, and Cloud deployment tools (uDeploy and Ansible); and tracking experimentation and model evaluation using data and model versioning tools (DVC and WandB). 

• DE developing software applications using Cloud platforms and establishing in-house patterns to deliver at scale; improving application performance and concurrency to support business rules and ensure observability and resiliency, using multi-threading models (for parallel processing); and migrating applications into Cloud-enabled APIs using Restful APIs, AWS EC2, and AWS ALB. 

• DE designing and developing multi-tier and Web enabled systems; designing and developing message broker interfaces throughout the Software Development Life Cycle (SDLC) according to Service Oriented Architecture (SOA); and enabling Web services security using Lightweight Directory Access Protocol (LDAP) servers and Two-Way SSL system authentication. 

#PE1M2 

#LI-DNI 

Certifications:

Category:

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.