Software Engineering SMTS – Tableau Cloud

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Salesforce is hiring for a Software Engineering SMTS – Tableau Cloud role, with opportunities in Bangalore and Hyderabad. This is a high-impact engineering opportunity for professionals interested in Generative AI, distributed systems, cloud infrastructure, microservices, Kubernetes, ML engineering and large-scale production platforms. The current opening is JR327532, listed as full-time with Office – Flexible work mode. The role specifically seeks professionals with 4+ years of ML engineering experience and experience building AI systems or services at scale. Locations: Bangalore / Hyderabad Experience: 4+ years ML Engineering Role: Software Engineering SMTS – Tableau Cloud Job ID: JR327532 Work Mode: Office – Flexible Employment: Full-time About Salesforce Salesforce is the world's leading AI CRM company, founded in 1999. Its platform brings together CRM, data, applications and AI agents, with Agentforce positioned as a major part of Salesforce's strategy for the agentic enterprise. Salesforce says more than 150,000 companies worldwide trust its platform. Why Is This Role Good for Experienced Engineers? This isn't simply an ML coding position. The JD combines machine learning engineering with distributed systems and production engineering. You could work on: Production-grade Generative AI services AWS/GCP cloud platforms Distributed microservices Kubernetes and containerized deployments Kafka and distributed messaging Spark and large-scale data processing PyTorch/TensorFlow MLOps and ML infrastructure Performance, monitoring and capacity planning Secure, reliable multi-tenant systems The role also requires collaboration with Product Managers, Architects, Data Scientists and Deep Learning Researchers, giving engineers exposure beyond implementation into architecture, product thinking and production AI. Skills Required Core Technical Skills Machine Learning Engineering Generative AI / Production AI Python Java and Spring are valuable for broader Salesforce engineering opportunities REST APIs and microservices Distributed systems AWS / GCP Kubernetes Docker Kafka Spark Hadoop Distributed databases and data processing Monitoring and observability ML & AI Skills MLOps ML infrastructure Model deployment Model serving Model monitoring PyTorch TensorFlow SageMaker Triton ML evaluation and experimentation Engineering Skills System design Scalability Reliability engineering Capacity planning Performance optimization Root-cause analysis CI/CD Production troubleshooting Technical documentation Preparation Tips – Unique to This JD Don't prepare this interview like a traditional Java or Python developer interview. The strongest preparation should connect ML + distributed systems + production engineering. 1. Prepare an End-to-End AI System Be ready to design a production AI service serving thousands of tenants. Discuss: API Gateway → authentication → model service → inference layer → data store → Kafka → monitoring → autoscaling. Explain how you would handle latency, failures, tenant isolation, model versioning and cost. 2. Study Kubernetes for ML Workloads Know the difference between pods, deployments, services, ingress, autoscaling and resource limits. Also understand how GPU-based inference workloads can be deployed and monitored. 3. Understand MLOps Prepare the complete lifecycle: Data → training → validation → model registry → deployment → inference → monitoring → retraining. Be prepared to discuss model drift, rollback, A/B testing and production monitoring. 4. Strengthen Distributed Systems Focus on Kafka partitions, consumer groups, replication, fault tolerance, caching, horizontal scaling and eventual consistency. 5. Don't Ignore Coding Salesforce MTS/SMTS interviews commonly include coding and computer-science fundamentals. Recent candidate reports mention DSA, low-level design and system design in Salesforce engineering interviews. Expected Salesforce Interview Rounds The exact process can vary by team and level, so the following should be treated as an expected structure, not an official guaranteed process. Round 1 – Recruiter/Initial Screening: Resume discussion, experience, motivation, location, compensation and role alignment. Round 2 – Coding / DSA: Expect algorithmic problem solving, data structures, complexity analysis and coding quality. Recent MTS reports specifically mention LeetCode-style coding. Round 3 – Technical / ML Engineering: Machine learning fundamentals, production ML, model deployment, APIs and your previous AI projects. Round 4 – System Design / Distributed Systems: Design scalable services, multi-tenant systems, messaging pipelines, storage and reliability architecture. Round 5 – Hiring Manager / Behavioral: Ownership, collaboration, customer focus, leadership and examples of taking systems from prototype to production. Some recent Salesforce engineering candidates have reported three to five stages, with combinations of coding, LLD, system design and hiring-manager discussions. Expected Salary Range Salesforce does not publish a salary range in the job information provided. As a market reference, Glassdoor's current Salesforce MTS data shows an India base-pay range of approximately ₹24–36 LPA, with an average around ₹31.3 LPA; recent Bengaluru submissions include a 4–6 year MTS profile at ₹42 LPA total pay. For this specialized SMTS/ML Engineering role, compensation can vary substantially based on level, AI expertise, experience and stock/bonus components. A reasonable target could be ₹35–55+ LPA total compensation, but candidates should confirm the actual package during the recruiter discussion. Resume Tips for This Salesforce JD Your resume should emphasize production impact, not simply list technologies. Instead of: "Worked on machine learning models." Write: "Developed and deployed ML inference services supporting production workloads, implementing monitoring and automated deployment pipelines." Highlight: Production AI systems Model deployment Scale/tenant numbers Latency improvements Cloud infrastructure Kubernetes Kafka/Spark MLOps Reliability improvements Cost optimization System-design contributions Quantify everything possible: requests/second, latency reduction, model accuracy, infrastructure cost reduction, deployment frequency or number of users/tenants served. How to Apply Application Link: Click Here The opening is Software Engineering SMTS – Tableau Cloud, JR327532, with locations in Bangalore and Hyderabad and flexible office work mode. Apply for Salesforce JR327532 Locations: Bangalore / Hyderabad Experience: 4+ years ML Engineering Role: Software Engineering SMTS – Tableau Cloud Job ID: JR327532 Work Mode: Office – Flexible Employment: Full-time Bottom line: If you have strong ML engineering experience and want to work at the intersection of Generative AI + Kubernetes + distributed systems + cloud + MLOps, this Salesforce opportunity is worth serious consideration. It offers the kind of engineering problems that can move your career from building individual models to operating AI systems at enterprise scale.