Senior AI/ML Engineer

Job Description:

Sr. Machine Learning Ops Engineer

As a Sr 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.

• Has bachelor's or master's Degree in a technology related field (e.g. Computer Science, Engineering, etc.).
• Proven 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. Familiarity with both Azure Cognitive Services, particularly for deploying OpenAI models, and Google Compute Vertex is beneficial.
• 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
• Extensive experience working with machine learning models with respect to deployment, inference, tuning, and measurement required.
• Strong knowledge of developing highly scalable distributed systems using Open-source technologies.
• 5+ years of proven experience in developing and implementing Python-based cloud applications and/or machine learning solutions.
• 1+ years of experience in developing ML infrastructure and MLOps in the Cloud using AWS Sagemaker.
• Proficiency in Python software development with strong experience in its ML ecosystem (numpy, pandas, sklearn, tensorflow, etc.), along with solid skills in Linux scripting. Ability to design and implement software using both object-oriented and functional programming paradigms. Basic knowledge of Java and Groovy is a plus.
• Strong knowledge of developing highly scalable distributed systems using Open-source technologies.
• Solid experience in Agile methodologies (Kanban and SCRUM).

• Designing & developing a feature generation & store framework that promotes sharing of data/features among different ML models.
• 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 a strong focus on 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 a Data Science and Big data ecosystem.

• 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).
• Exploring new technology trends and leveraging them to simplify our data and ML ecosystem.
• Designing & developing a feature generation & store framework that promotes sharing of data/features among different ML models.
• 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.
• 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.