Salesforce is hiring for a Software Engineering SMTS β 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 β Flexible work mode. The role specifically seeks professionals with 4+ years of ML engineering experience and experience building AI systems or services at scale.
π Locations: Bangalore / Hyderabad
πΌ Experience: 4+ years ML Engineering
π― Role: Software Engineering SMTS β Tableau Cloud
π Job ID: JR327532
π’ Work Mode: Office β Flexible
β° Employment: Full-time
π’ 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.
β 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:
π€ Production-grade Generative AI services
βοΈ AWS/GCP cloud platforms
βοΈ Distributed microservices
π¦ Kubernetes and containerized deployments
π¨ Kafka and distributed messaging
π₯ Spark and large-scale data processing
π§ PyTorch/TensorFlow
π MLOps and ML infrastructure
π Performance, monitoring and capacity planning
π 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.
π οΈ 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
π§ Preparation Tips β 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.
1οΈβ£ Prepare an End-to-End AI System
Be ready to design a production AI service serving thousands of tenants.
Discuss:
API Gateway β authentication β model service β inference layer β data store β Kafka β monitoring β autoscaling.
Explain how you would handle latency, failures, tenant isolation, model versioning and cost.
2οΈβ£ 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.
3οΈβ£ Understand MLOps
Prepare the complete lifecycle:
Data β training β validation β model registry β deployment β inference β monitoring β retraining.
Be prepared to discuss model drift, rollback, A/B testing and production monitoring.
4οΈβ£ Strengthen Distributed Systems
Focus on Kafka partitions, consumer groups, replication, fault tolerance, caching, horizontal scaling and eventual consistency.
5οΈβ£ 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.
π― 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 β Recruiter/Initial Screening:
Resume discussion, experience, motivation, location, compensation and role alignment.
Round 2 β Coding / DSA:
Expect algorithmic problem solving, data structures, complexity analysis and coding quality. Recent MTS reports specifically mention LeetCode-style coding.
Round 3 β Technical / ML Engineering:
Machine learning fundamentals, production ML, model deployment, APIs and your previous AI projects.
Round 4 β System Design / Distributed Systems:
Design scalable services, multi-tenant systems, messaging pipelines, storage and reliability architecture.
Round 5 β 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.
π° 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 βΉ24β36 LPA, with an average around βΉ31.3 LPA; recent Bengaluru submissions include a 4β6 year MTS profile at βΉ42 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 βΉ35β55+ LPA total compensation, but candidates should confirm the actual package during the recruiter discussion.
π Resume Tips for This Salesforce JD
Your resume should emphasize production impact, not simply list technologies.
Instead of:
β βWorked on machine learning models.β
Write:
β βDeveloped and deployed ML inference services supporting production workloads, implementing monitoring and automated deployment pipelines.β
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.
π© How to Apply
π Application Link: Click Here
The opening is Software Engineering SMTS β Tableau Cloud, JR327532, with locations in Bangalore and Hyderabad and flexible office work mode.
π Locations: Bangalore / Hyderabad
πΌ Experience: 4+ years ML Engineering
π― Role: Software Engineering SMTS β Tableau Cloud
π Job ID: JR327532
π’ Work Mode: Office β Flexible
β° Employment: Full-time
π₯ 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.
