Principal Machine Learning Engineer (Agentic AI for Cloud Security)

Quick Facts

• Role: Principal Machine Learning Engineer (Agentic AI for Cloud Security)

Description

Provide technical leadership to architect and deliver next-generation, AI-driven cloud security solutions. You will design and implement agentic ML/GenAI systems, build and fine-tune advanced LLMs/SLMs (including multimodal models), and oversee the full lifecycle of high-reliability Security-as-a-Service (SECaaS) offerings. You will also optimize GPU training and inference and integrate real-time endpoint telemetry into data pipelines and agentic/analytical workflows.

Responsibilities

• Architect agentic systems with dynamic planning, multi-step execution, self-correction, and multi-agent + human-in-the-loop collaboration

• Build, evaluate, and fine-tune LLMs and SLMs; diagnose regressions (e.g., data distribution shift, alignment failures, loss anomalies) and recover quality via targeted fine-tuning

• Profile, debug, and optimize model training/inference on GPU clusters (memory bottlenecks, distributed communication overhead, utilization)

• Design scalable data preprocessing and feature extraction pipelines for real-time endpoint telemetry

• Drive prototype-to-production engineering and establish best practices (prompt architecture, agent memory persistence, context window optimization, tool-use execution, safety guardrails)

• Evaluate agent frameworks, model serving engines, and accelerator architectures to inform technical roadmaps

Requirements

• 7+ years of professional software engineering with experience deploying production-grade ML/GenAI/agentic architectures at enterprise scale

• Advanced Python for AI/ML development plus production-level Go or Java for distributed microservices

• Hands-on data pipeline engineering experience; Apache Spark and SQL

• BigQuery schema design experience and Google Cloud infrastructure experience (Dataproc, GKE, Google Cloud Platform SDKs)

• Operational expertise with both LLMs and SLMs, including diagnosing performance issues and running remedial fine-tuning (dataset curation, parameter-efficient fine-tuning, loss monitoring, alignment, evaluation benchmarks)

• Practical GPU profiling and debugging experience (CUDA memory optimization, multi-GPU synchronization, batch throughput tuning, handling GPU stalls/crashes)

• Solid understanding of Retrieval-Augmented Generation (RAG), vector storage, prompt optimization, evaluation metrics, and applied statistical modeling

• BS or MS in Computer Science, Data Science, Software Engineering, or related field

Benefits

• Compensation range: $163,200.00 - $264,000.00 per year (base salary; RSUs and bonus may be included)

• Reasonable accommodations available; equal opportunity employer; immigration sponsorship: Yes