Performance Engineer - Mts

Salesforce Salesforce · Enterprise · Hyderabad, India

Software Engineer role focused on building internal AI-powered productivity tools and agentic experiences to solve performance problems for engineering teams. Responsibilities include developing AI agents for triaging issues, automating performance analysis, and instrumenting systems to measure AI agent performance. Requires strong software engineering fundamentals, ML/AI knowledge (LLMs, NLP, inference, fine-tuning, agentic workflows), and Python experience.

What you'd actually do

  1. Contribute to the development of internal AI-based productivity tools that provide agentic experiences to help solve performance problems and enhance the efficiency of internal engineering teams (e.g., assist in tools for reducing mean time to resolve performance issues, anomaly detection, and exploring auto-resolution).
  2. Support investigations into performance anomalies to help identify application bottlenecks and apply the learning to develop AI-powered agents to assist with initial triaging.
  3. Build intelligent frameworks that automate performance anomaly detection, infrastructure health checks, and root cause identification across telemetry, logs, and system utilization metrics.
  4. Actively learn about and explore emerging AI and web technologies and work closely with senior team members on advancements in AI, machine learning, and software development best practices to identify potential opportunities for tool improvements.
  5. Help implement and execute methodologies for evaluating performance and scalability using defined representative user workloads and contribute to internal and external performance benchmarks.

Skills

Required

  • Python
  • AI applications
  • strong software engineering fundamentals (data structures, algorithms, design patterns)
  • Deep understanding of machine learning/AI fundamentals, especially LLMs and NLP concepts, including inference orchestration, fine-tuning, and agentic workflows
  • Strong technical problem-solving, communication, and collaboration skills, with a focus on AI-related challenges and the ability to work effectively with diverse teams.
  • Intense curiosity and willingness to question and explore new AI technologies.

Nice to have

  • Master's in Computer Science, Engineering, Artificial Intelligence, Machine Learning, or a related technical field.
  • Knowledge of various prompting strategies (e.g., zero-shot, few-shot, chain-of-thought) to elicit desired responses from AI models, especially Large Language Models (LLMs).
  • Familiarity with NLP concepts like tokenization, syntax, semantics, different language models, and generative AI.
  • Contributions to open-source AI, front-editor, or performance-related projects.
  • Experience with ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
  • Familiarity with AI agent frameworks like LangChain, Semantic Kernel, or AutoGen.
  • Familiarity with telemetry and production analysis (metrics, traces, logs), and applying AI to solve observability or debugging challenges.
  • Familiarity with profiling tools (e.g., perf, FlameGraphs) and performance diagnostic techniques.

What the JD emphasized

  • AI-powered productivity tools
  • agentic experiences
  • AI Agents
  • performance problems
  • LLM orchestration
  • AI-based productivity tools
  • agentic experiences
  • performance problems
  • AI-powered agents
  • intelligent frameworks
  • AI and web technologies
  • AI, machine learning
  • evaluating performance and scalability
  • AI agents
  • performance
  • AI applications
  • machine learning/AI fundamentals
  • LLMs and NLP concepts
  • inference orchestration
  • fine-tuning
  • agentic workflows
  • AI-related challenges
  • AI technologies

Other signals

  • AI Agents
  • LLM orchestration
  • performance problems
  • internal engineering teams
  • agentic experiences