Software Engineering Smts - AI Cloud

Salesforce Salesforce · Enterprise · Bangalore, India, India

This role focuses on engineering scalable generative AI services within Salesforce's AI Cloud. It involves designing, delivering, and optimizing these services for production environments, with a strong emphasis on distributed systems, cloud infrastructure (AWS/GCP), Kubernetes, and MLOps. The role requires experience in ML engineering and building AI systems, with a secondary focus on data aspects for ML.

What you'd actually do

  1. Design and deliver scalable generative AI services that can be integrated with many applications, thousands of tenants, and run at scale in production.
  2. Drive system efficiencies through automation, including capacity planning, configuration management, performance tuning, monitoring and root cause analysis.
  3. Participate in periodic on-call rotations and be available for critical issues.
  4. Partner with Product Managers, Application Architects, Data Scientists, and Deep Learning Researchers to understand customer requirements, design prototypes, and bring innovative technologies to production.

Skills

Required

  • ML engineering in building AI systems and/or services
  • designing and building distributed microservices on AWS, GCP or other public cloud substrates
  • modern containerized deployment stack using Kubernetes, Spinnaker, and other technologies
  • implement, operate, and deliver results via innovation at large scale
  • distributed, scalable systems and modern data storage, messaging and processing frameworks, including Kafka, Spark, Docker, Hadoop, etc.

Nice to have

  • solve problems that the world has not solved before
  • cultivating strong working relationships and driving collaboration across multiple technical and business teams
  • MLOps/ML Infra workflows, processes and ML components
  • building and applying machine learning models for business applications
  • Sagemaker, Tensorflow, Pytorch, Triton, Spark, or equivalent large-scale distributed Machine Learning technologies

What the JD emphasized

  • 4+ years of industry experience of ML engineering in building AI systems and/or services.
  • Experience designing and building distributed microservices on AWS, GCP or other public cloud substrates.
  • Proven ability to implement, operate, and deliver results via innovation at large scale.
  • Experience with distributed, scalable systems and modern data storage, messaging and processing frameworks, including Kafka, Spark, Docker, Hadoop, etc.
  • Understanding of MLOps/ML Infra workflows, processes and ML components.
  • Strong experience building and applying machine learning models for business applications.

Other signals

  • design and deliver scalable generative AI services
  • integrate with many applications, thousands of tenants, and run at scale in production
  • ML engineering in building AI systems and/or services
  • distributed, scalable systems
  • MLOps/ML Infra workflows
  • building and applying machine learning models for business applications