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AI Tech Lead - Staff Machine Learning Engineer
AI Tech Lead - Staff Machine Learning Engineer
As an AI Tech Lead - Staff Machine Learning Engineer, you will lead the design and delivery of next-generation Agentic AI systems for Security Operations Centers (SOC). This highly technical leadership role involves architecting, prototyping, and productionizing multi-agent AI systems, evaluating AI technologies, and driving LLM fine-tuning efforts for real-time detection, investigation, and response.
About the role
About the Role
As an AI Tech Lead, you will lead the design and delivery of the next generation of Agentic AI systems for the Security Operations Center (Agentic SOC). You will evaluate, prototype, and productionize state-of-the-art agentic AI technologies and build scalable multi-agent architectures that reason over large-scale machine data to drive real-time detection, investigation, and response. This is a highly technical leadership role with deep ownership of AI agent architecture, evaluation, LLM fine-tuning, and production AI infrastructure. You will help define the technical direction for Sumo Logic’s agentic AI platform and play a key role in bringing advanced AI capabilities to customers at global scale.
Responsibilities
- 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.
Required Qualifications
- 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.
Desired Qualifications
- Hands-on experience building and scaling agentic AI systems or multi-agent architectures in production.
- Experience with modern agent frameworks such as LangGraph, LangChain, CrewAI, or similar.
- Experience with major foundation model platforms such as Anthropic, OpenAI, AWS Bedrock, or Vertex AI.
- Experience with LLM fine-tuning pipelines (SFT, RLHF/RLAIF, preference learning, domain adaptation).
- Strong background in LLMOps, including inference optimization, latency/cost management, observability, and production monitoring.
- Experience with ML infrastructure and tooling such as PyTorch, MLflow, Airflow, Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure).
- Experience applying AI/ML to security, observability, or large-scale log/telemetry data is a strong plus.