Senior Machine Learning 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. Builds and deploys data centric RESTful Java APIs. Uses ETL data pipelines and workflow tools -- Luigi and Airflow, and Cloud Technologies -- Docker, Kubernetes, AWS CLI, AWS CloudFormation and AWS services. Delivers high quality data solutions in a multi-developer environment. Performs deep data analysis on multiple database platforms using SQL skills. Builds data pipelines to evaluate ML models, using Apache and Spark. Writes code with object-oriented/object scripting languages -- Python, Java, C++, and Scala. 

Primary Responsibilities: 

• Performs root cause analysis on internal and external data processes to answer business questions. 

• Applies complex statistical techniques and concepts to data analysis. 

• Collaborates with senior managers and decision makers to identify and solve a variety of problems and to clarify management objectives. 

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

• Analyzes information to determine, recommend, and plan computer software specifications on major projects and proposes modifications and improvements based on user need. 

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

Education and Experience: 

Bachelor’s degree (or foreign education equivalent) in Computer Science, Computational Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and three (3) years of experience as a Senior Machine Learning Engineer (or closely related occupation) performing real time analytics and developing personalized recommendation applications using Amazon Web Services (AWS). 

Or, alternatively, Master’s degree (or foreign education equivalent) in Computer Science, Computational Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and one (1) year of experience as a Senior Machine Learning Engineer (or closely related occupation) performing real time analytics and developing personalized recommendation applications using Amazon Web Services (AWS). 

Skills and Knowledge: 

Candidate must also possess: 

• Demonstrated Expertise (“DE”) performing Object Oriented Programming (Java, Scala, and Python) within Python’s ML ecosystem including using NumPy, Panda, sklearn, TensorFlow and Amazon Web Services (AWS) to build the ML Infrastructure and MLOPs pipelines to deploy the recommendation models. 

• DE building data pipelines and getting the data required to build and evaluate ML models using Apache Spark, Snowflake, and AWS Glue. 

• DE architecting and developing personalized recommendation applications using AWS Sagemaker, Data movement technologies (ETL/ELT), Messaging and Streaming Technologies (AWS SQS, Kinesis, and Kafka), Relational and NoSQL databases (DynamoDB, Postgres, and Graph database), and API and in-memory technologies. 

• DE automating the development processes using CI/CD tools Deployment Pipeline (Jenkins), version control (GitHub), orchestration and DAGs tools (AWS Step Functions, Airflow, Luigi, and Kubeflow), and app hosting services (ECS or EKS). 

#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.