Staff Machine Learning Engineer – AI Tech Lead

Role Overview

• 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.

Requirements

• 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.

Tech Stack

• AWS
• Distributed Systems
• Python

Benefits

• 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.