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Job Category
Software Engineering

Job Details

About Salesforce

We're Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too - driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good - you've come to the right place.

• We are looking for passionate MLOps Engineers to help us take us to the next level, and support and collaborate with MLE and Data Science team to build a platform that scales to hundreds of thousands of customers, and hundreds of billions of predictions per day and works on bleeding edge technologies on model training, model inferencing and Generative AI. In this position, you will play a crucial role in bridging the gap between machine learning development and operational reliability. You will be responsible for ensuring the seamless integration of machine learning models into our production environment while maintaining high availability, scalability, and reliability. This role requires a deep understanding of both machine learning concepts and modern DevOps/SRE practices.
  
  Your Impact:
• Lead the charge on taking our core platform tools to the next level in terms of engineering maturity and architecture.
• Refine and develop new workflows, tools, and automation.
• Build tools to monitor machine learning pipelines and services, data pipeline performance, data quality and models in production.
• Establish best practices with coding standards, workflows, tools, and product automation.
• Review and maintain existing tool-set and codebase (pipelines, models, algorithms); continue to improve existing tools and build new ones.
• Scale the operations of the MLE team by building automation and libraries.
  
  The ideal candidate will be:
• Technical - We are looking for passionate and code geek developers who analyze business problems and evolve technical solutions in the most optimal and simple ways. Sometimes engineers wear multiple hats to drive their projects end-to-end, thinking holistically and compare from available set of technologies to drive best decisions technically.
• Team Player - You will drive collaboration, efficiency and communication by liaising with your peers, leadership, product and program management and cross-teams. You will support/seek timely help with your peers, communicate risks and mitigation plans with leadership, and communicate closely with product managers to iteratively build AI Platform services that cater to our users and business use cases.
• Working with Sagemaker, Tensorflow, Pytorch, Triton, Spark, or equivalent large-scale distributed Machine Learning technologies on a modern containerized deployment stack using Kubernetes, Spinnaker, and other technologies.
• Partner with Product Managers, Architects, Machine Learning Engineers and Software Engineers to understand platform requirements, and help translate requirements to working software.
• Own the ML DevOps for fully orchestrated machine learning APIs for the Einstein Platform.
• Contribute to the long-range plan, and help drive the efficient platform architectures for machine learning.
• Participate in the team's on-call rotation to address complex problems in real-time and keep services operational and highly available.
• Create and enforce processes that ensure quality of work, and drive engineering excellence.
• Exhibit a customer-first mentality while making decisions, and be responsible and accountable for the output of the team.
• Work collaboratively in geographically distributed teams in North America, EMEA and APAC
  
  Core Qualifications:
• 2+ years of industry experience and a passion for crafting, analyzing and deploying machine learning-based solutions
• Experience working as part of a team with mature data science products
• Consistent record in building and establishing comprehensive monitoring, logging, and alerting solutions to proactively identify and address performance bottlenecks and potential issues, ensuring the continuous availability and reliability of our machine learning systems.
• Experience deploying, monitoring and maintaining data science products in cloud environments such as AWS or Microsoft Azure
• Good understanding of Machine Learning methods, including ML project lifecycle and associated challenges at each stage of development.
• Proficient at writing good quality, well-documented and tested, scalable code - Python preferred. Experience with tools like mlFlow, Airflow, Docker and Cloud Platforms such as AWS/GCP is ideal
• Strong grasp of DevOps best practices, including continuous integration, continuous deployment, and infrastructure automation, supported by practical experience in implementing and managing CI/CD pipelines.
• Act as a first responder to production incidents, utilizing your troubleshooting skills and expertise to swiftly diagnose and resolve issues, minimizing downtime and mitigating potential impact on our operations.
• Strong communication skills and ability to interface well with other engineers, data scientists and product managers
• Passion, curiosity, solutions focus and independence
• Preferred Qualifications
• Experience in DevOps specific to Salesforce Einstein and Data Cloud platform, including deployment and maintenance of Salesforce applications and integration with machine learning models.
• Thorough understanding of networking concepts and protocols, with the ability to design and troubleshoot complex network architectures.