Machine Learning Engineer engaged in recommendation and search, NLP, and image processing. Contributing to a major online dating app, Pairs, in Japan.
SF
Hybrid Principal AI/ML Engineer
Posted 3 months ago
About the role
- Principal Machine Learning Ops Engineer developing scalable ML infrastructure and analytics platforms for financial services. Collaborating with Data Scientists to drive model deployment and optimization.
Responsibilities
- 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
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
- Familiarity with both Azure Cognitive Services, particularly for deploying OpenAI models, and Google Compute Vertex is beneficial
- 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)
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
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