Performance Engineer - MTS

Description

Software Engineer - Member of Technical Staff (MTS): AI Productivity Tools

Job Location: Bangalore / Hyderabad

Software Engineering MTS: AI Productivity Tools & Performance Engineering

As a Software Engineer with a focus on AI-powered productivity tools, you will play a key role in innovating and developing applications that leverage artificial intelligence to enhance user workflows and efficiency for internal engineering teams. You will be responsible for designing, building, and deploying intelligent internal tools that provide agentic experiences and solve complex performance problems through AI Agents. Collaboration with various engineering groups, product, and AI research teams is crucial to achieve these goals.

You will dive into complex performance and scalability challenges, design innovative solutions, and build AI-driven applications from the ground up. Your role will involve developing next-generation tools that automate tasks, provide intelligent insights, and improve overall user productivity across the software development lifecycle (SDLC). This includes experimenting with various AI models, developing robust systems for performance, analyzing and visualizing data, and conducting end-to-end performance analysis.

Your work will directly contribute to faster resolution of production issues, improved reliability of services, and productive use of engineering bandwidth. You will rapidly prototype and iterate on ideas from across the team, bringing AI capabilities into internal workflows using the latest developments in AI Agents, LLM orchestration, and intelligent automation.

Responsibilities

• 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).
• Support investigations into performance anomalies to help identify application bottlenecks and apply the learning to develop AI-powered agents to assist with initial triaging.
• Build intelligent frameworks that automate performance anomaly detection, infrastructure health checks, and root cause identification across telemetry, logs, and system utilization metrics.
• 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.
• Help implement and execute methodologies for evaluating performance and scalability using defined representative user workloads and contribute to internal and external performance benchmarks.
• Instrument and benchmark systems to measure latency, reliability, and accuracy of AI agents; analyze gaps and continuously tune for performance.
• Clearly communicate your work, findings, and analysis to team members and present recommendations effectively to technical audiences.
• Participate in product design discussions and help address software performance issues by identifying hotspots, develop prototypes to ensure
  
  

• A related technical degree required.
• 4+ years of experience in coding (Python highly preferred, Java/C++) with a focus on AI applications and 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.
  
  

• Masters 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-end, 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.