Principal, AI/ML Engineer

Fidelity Investments • Jersey City, NJ

7d • Hybrid

About The Position

Principal Machine Learning Ops Engineer As a Principal Machine Learning Ops Engineer within the Enterprise Data Science Platform team, you will create frameworks to support large-scale ML infrastructure and pipelines, including tools for the containerization and deployment of ML models. Collaborating with Data Scientists, you will develop advanced analytics and machine learning platforms to enable the prediction and optimization of models. You will extend existing ML platforms for scaling model training and deployment, and partner with various business and engineering teams to drive the adoption and integration of model outputs. This role is essential in leveraging Data Science to deliver exceptional customer experiences in financial services. The Team The enterprise data science platform (part of the Fidelity Data Architecture team in the Enterprise Technology BU) is focused on delivering AI/ML solutions for the organization. As part of this team, you will be responsible for building advanced cloud and software solutions in collaboration with Data Scientists to support packaging, deployment, and scaling of AI/ML Models in production.

Requirements

• Has bachelor’s or master’s Degree in a technology related field (e.g. Engineering, Computer Science, etc.).
• 8+ years of proven experience in developing and implementing Python-based cloud applications and/or machine learning solutions.
• 2+ years of experience in developing ML infrastructure and MLOps in the Cloud using AWS Sagemaker.
• 5+ years of experience in building cloud-native applications using a range of AWS services, including but not limited to SageMaker AI, Bedrock, S3, CloudFormation (CFT), SNS, SQS, Lambda, AWS Batch, Step Functions, EventBridge, and CloudWatch.
• 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.
• Strong experience with CI/CD tools, particularly Jenkins, for automating and streamlining the software development pipeline.
• Proficient in using version control systems like Git for effective code management and collaboration.
• Hands-on experience with containerization technologies such as Docker for building and deploying applications.
• Expertise in infrastructure as code (IaC) services, including AWS CloudFormation and tools like Terraform or OpenTofu, for managing and provisioning cloud resources
• Solid experience in Agile methodologies (Kanban and SCRUM).
• You have strong technical design and analysis skills.
• You the ability to deal with ambiguity and work in fast paced environment.
• Your experience supporting critical applications.
• You are familiar with applied data science methods, feature engineering and machine learning algorithms.
• Your Data wrangling experience with structured, semi-structure and unstructured data.
• Your experience building ML infrastructure, with an eye towards software engineering.
• You have excellent communication skills, both through written and verbal channels.
• You have excellent collaboration skills to work with multiple teams in the organization.
• Your ability to understand and adapt to changing business priorities and technology advancements in Big data and Data Science ecosystem.

Nice To Haves

• Familiarity with both Azure Cognitive Services, particularly for deploying OpenAI models, and Google Compute Vertex is beneficial.

Responsibilities

• Designing & developing a feature generation & store framework that promotes sharing of data/features among different ML models.
• Partner with Data Scientists and to help use the foundational platform upon which models can be built and trained.
• Operationalize ML Models at scale (e.g. Serve predictions on tens of millions of customers).
• Build tools to help detect shifts in data/features used by ML models to help identify issues in advance of deteriorating prediction quality, monitoring the uncertainty of model outputs, automating prediction explanation for model diagnostics.
• Exploring new technology trends and leveraging them to simplify our data and ML ecosystem.
• Driving Innovation and implementing solutions with future thinking.
• Guiding teams to improve development agility and productivity.
• Resolving technical roadblocks and mitigating potential risks.
• Delivering system automation by setting up continuous integration/continuous delivery pipelines.

Benefits

• comprehensive health care coverage and emotional well-being support
• market-leading retirement
• generous paid time off and parental leave
• charitable giving employee match program
• educational assistance including student loan repayment, tuition reimbursement, and learning resources to develop your career

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Number of Employees: 5,001-10,000 employees