
501 - 1000 employees
Founded 2010
☁️ SaaS
🔒 Cybersecurity
💰 $110M Series G on 2019-05
SaaS • Cybersecurity • Cloud Computing
Sumo Logic is a cloud-based machine data analytics company that offers a comprehensive platform for monitoring, troubleshooting, automating, and defending IT infrastructures. The company specializes in cloud security intelligence, leveraging AI and machine learning to provide enhanced observability and security threat detection. It is known for its powerful log management capabilities, which help organizations unlock cloud security and efficiently troubleshoot system issues. Sumo Logic's platform integrates seamlessly with cloud services, providing infrastructure monitoring and application observability to modernize IT operations. Their solutions are tailored to various use cases, including DevSecOps, cloud migration, and digital customer experience enhancement.
🕒 March 4
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501 - 1000 employees
Founded 2010
☁️ SaaS
🔒 Cybersecurity
💰 $110M Series G on 2019-05
SaaS • Cybersecurity • Cloud Computing
Sumo Logic is a cloud-based machine data analytics company that offers a comprehensive platform for monitoring, troubleshooting, automating, and defending IT infrastructures. The company specializes in cloud security intelligence, leveraging AI and machine learning to provide enhanced observability and security threat detection. It is known for its powerful log management capabilities, which help organizations unlock cloud security and efficiently troubleshoot system issues. Sumo Logic's platform integrates seamlessly with cloud services, providing infrastructure monitoring and application observability to modernize IT operations. Their solutions are tailored to various use cases, including DevSecOps, cloud migration, and digital customer experience enhancement.
• Lead and partner with fellow leadership members and teams on technical evaluation and adoption of cutting-edge agentic AI platforms, including Anthropic (Claude), LangChain/LangGraph, AWS Bedrock, and other emerging agent frameworks. • Architect, prototype, and productionize multi-agent AI systems for Agentic SOC use cases, including detection, triage, investigation, and response workflows. • Own the design of core agent architecture components, including planning, execution, tool orchestration, memory, context engineering, and long-running agent workflows. • Lead AI agent evaluation systems, including offline and online evaluation pipelines, golden datasets, synthetic data generation, human- and LLM-based judging, and continuous quality monitoring. • Drive LLM fine-tuning and alignment efforts to improve domain-specific reasoning, accuracy, and reliability for security and observability use cases. • Design scalable LLMOps and AI agent infrastructure, including inference routing, latency optimization, cost control, and production observability for agent systems. • Partner with product, security, and data platform leadership and teams to deliver end-to-end AI agent capabilities from prototype to customer-facing production systems. • Lead and partner on technical direction and mentorship for AI engineers working on agentic AI and LLM systems. • Define and implement best practices for AI safety, reliability, evaluation, and monitoring in production agentic systems. • Operate as a senior technical owner in ambiguous problem spaces—setting technical direction, breaking down complex problems, and driving delivery across teams.
• B.Tech, M.Tech, or Ph.D. in Computer Science, Machine Learning, Data Science, or a related technical field. • 5+ years of hands-on industry experience building, operating, and leading production ML/AI systems, with demonstrated technical leadership and ownership. • Strong foundation in machine learning, distributed systems, data pipelines, and large-scale system design. • Deep industry understanding of LLMs, prompt engineering, context engineering, agentic AI design patterns, and reasoning workflows. • Strong proficiency in Python and modern ML/AI ecosystems. • Experience designing and operating evaluation frameworks for ML/LLM systems (offline + online). • Proven ability to lead complex technical initiatives across teams and influence architecture decisions. • Excellent communication skills and ability to translate complex AI systems into business impact.
• Compensation varies based on a variety of factors, which include (but aren’t limited to) role level, skills and competencies, qualifications, knowledge, location, and experience. • In addition to base pay, certain roles are eligible to participate in our bonus or commission plans, as well as our benefits offerings and equity awards.
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