Machine Learning Engineer working on next - generation agentic AI platform at Salesforce. Collaborate with teams to innovate and design impactful AI systems for customers.
SF
Hybrid Senior AI/ML Engineer
Posted last month
About the role
- Senior ML Ops Engineer creating frameworks to support ML infrastructure and collaborate with data scientists. Focus on AI/ML solutions in financial services, enhancing customer experiences.
Responsibilities
- 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
- 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. 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)
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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FI
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