Machine Learning Engineer

Skills

Description

What does your team do/team size/ project details/ what are you trying to accomplish for Fidelity overall?
PI Data Engineering team (part of Personal Investing Technology BU) is focused on delivery data and ML solutions for the organization. As part of this team, you will be responsible for building advanced analytics solutions using various cloud technologies and collaborating with Data Scientists to robustly scale up ML Models to large volumes in production.

Responsibilities

• • Bachelor’s or Master’s Degree in a technology related field (e.g. Engineering, Computer Science, etc.)
• • 8+ years of proven experience in implementing Big data solutions in data analytics space
• • 2+ years of experience in developing ML infrastructure and MLOps in the Cloud using AWS Sagemaker
• • Extensive experience working with machine learning models with respect to deployment, inference, tuning, and measurement required
• • Experience in Object Oriented Programming (Java, Scala, Python), SQL, Unix scripting or related programming languages and exposure to some of Python’s ML ecosystem (numpy, panda, sklearn, tensorflow, etc.)
• • Experience with building data pipelines in getting the data required to build and evaluate ML models, using tools like Apache Spark or other distributed data processing frameworks
• • Data movement technologies (ETL/ELT), Messaging/Streaming Technologies (AWS SQS, Kinesis/Kafka), Relational and NoSQL databases (DynamoDB, EKS, Graph database), API and in-memory technologies
• • Strong knowledge of developing highly scalable distributed systems using Open-source technologies
• • Experience with CI/CD tools (e.g., Jenkins or equivalent), version control (Git), orchestration/DAGs tools (AWS Step Functions, Airflow, Luigi, Kubeflow, or equivalent)
• • Solid experience in Agile methodologies (Kanban and SCRUM)
• • Strong technical design and analysis skills
• • Ability to deal with ambiguity and work in fast paced environment
• • Experience supporting critical applications
• • Familiarity with applied data science methods, feature engineering and machine learning algorithms
• • Data wrangling experience with structured, semi-structured and unstructured data
• • Experience building ML infrastructure, with an eye towards software engineering
• • Excellent communication skills, both through written and verbal channels
• • Excellent collaboration skills to work with multiple teams in the organization
• • Ability to understand and adapt to changing business priorities and technology advancements in Big data and Data Science ecosystem