Description

Role Description

The AI Platform Performance, Scale & Cost-to-Serve (CTS) team is seeking a Senior Manager, Performance Engineering to lead performance, scalability, and cost optimization for Agentforce AI and AI Cloud platforms.

In this role, you will define and execute the end-to-end performance strategy across agentic AI systems, LLM workflows, and multi-model ecosystems, ensuring enterprise-grade latency, scalability, reliability, and cost efficiency.

You will lead a high-impact team focused on benchmarking, optimization, CTS/TCO modeling, and capacity engineering, supporting AI systems that power millions of transactions globally.

Join a highly technical and collaborative team at the forefront of Agentic AI innovation. As part of Salesforce’s GenAI initiatives, this team is responsible for scaling and optimizing LLM-driven applications, trust models (toxicity, PII), and agent workflows across a global footprint.

The team partners closely with Engineering, Infrastructure, Product, and Data Science to deliver performance excellence at scale, enabling customers to maximize the value of AI across the Salesforce ecosystem.

Key Responsibilities

• People Leadership & Execution
• Lead, mentor, and grow a team of performance engineers and SREs
• Define team vision, roadmap, and execution aligned with AI Platform strategy
• Drive hiring, onboarding, and retention of top engineering talent
• Establish best practices, frameworks, and standards across performance engineering
• Manage prioritization, resource allocation, and delivery across multiple programs
• Foster a culture of ownership, innovation, and operational excellence

Performance & Scalability Engineering

• Own performance strategy for Agentforce AI systems, ensuring scalability for millions of transactions with low latency and high availability
• Define and track key SLIs/SLOs (TTFT, P95/P99 latency, throughput, error rates)
• Optimize end-to-end agent workflows (planner → LLM → tools → actions)
• Drive architectural improvements across application, infrastructure, and model layers

Benchmarking & Optimization

• Lead benchmarking of GenAI models and agentic workflows across multiple providers
• Identify bottlenecks across GPU utilization, inference pipelines, APIs, and storage systems
• Leverage production telemetry and controlled experiments to validate performance improvements
• Continuously improve system efficiency balancing performance, cost, and quality

Automation, Tooling & Observability

• Build scalable performance automation frameworks integrated with CI/CD pipelines
• Develop tooling for benchmarking, regression detection, and performance analytics
• Implement observability across distributed systems using tools such as k6, Locust, JMeter, Splunk, Grafana, and APM platforms
• Enable continuous performance validation in pre-prod and production environments
• Knowledge of Unit of Scale/ Cost-to-Serve (CTS)

Cross-Functional Leadership

• Partner with Product, Engineering, Infrastructure, Data Science, and FinOps teams
• Influence architecture decisions to improve scalability, reliability, and cost efficiency
• Translate business goals into actionable performance and cost strategies
• Drive alignment across geo-distributed teams and stakeholders

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
• 10+ years of experience in software or performance engineering
• 5+ years of experience leading high-performing engineering teams
• Strong experience with large-scale distributed systems and cloud platforms (AWS, GCP, Azure)
• Hands-on experience in performance testing, benchmarking, and optimization
• Experience with AI/LLM systems, GPU workloads, or ML infrastructure
• Proficiency in programming languages such as Python or Java
• Experience with CI/CD, microservices architectures, and observability systems
• Strong analytical skills with ability to drive insights from production data
• Excellent communication and cross-functional leadership skills

Preferred Qualifications

• Experience with Agentic AI systems or LLM orchestration frameworks
• Deep understanding of LLM performance metrics (latency, token usage, throughput)
• Experience with performance tools (k6, JMeter, Locust)
• Familiarity with observability platforms (Splunk, Grafana, Prometheus, APM)
• Knowledge of data system performance (SQL, data lakes, pipelines)
• Experience in FinOps, cloud cost optimization, and capacity modeling
• Familiarity with containerized environments (Docker, Kubernetes) and deployment systems

Location

• Bangalore, India