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Strong Salesforce brand and metro location, but senior, specialized data+ML role reduces applicant density.
Requires deep data engineering, cloud, security, and LLM experience, limiting cross-industry transferability.
Many mandatory technical skills across distributed systems, data pipelines, cloud, and applied ML/LLM.
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Lead design and architecture of a scalable, secure data platform integrating security signals from 10+ Salesforce and 15+ external environments (AWS, GCP, CRM, etc.).
Develop and optimize data pipelines using technologies like Apache Spark, Kafka, and Docker (EKS) for processing diverse security data.
Integrate AI/ML and LLM-driven features such as RAG, anomaly detection, agentic triage, focusing on security and cost/latency tradeoffs, while providing technical leadership and mentoring.
Experience: Senior-level engineering with expertise in distributed systems, large-scale infrastructures, and data engineering.
Technical skills: Proficiency in at least one language (Python, Golang, Java/Scala), Apache Spark, Kafka, Hadoop, SQL/NoSQL, Docker (especially EKS), Terraform, Puppet, Linux (CentOS/RHEL).
Security fundamentals: Authentication/authorization frameworks (SSO, SAML, OAuth), secure transport (TLS), identity management (certificates, PKI).
Applied AI/ML: Hands-on experience integrating LLMs/ML models in production including RAG, agent/tool-use frameworks, prompt engineering, evals or embeddings/vector stores.
Strong background in architecting and operating high-scale, fault-tolerant distributed data platforms in cloud-native environments.
Proven experience applying AI/ML techniques to enhance security data analytics and workflows, including LLM integration and agentic coding tools.
Ability to lead cross-functional teams and mentor engineers while aligning technical solutions with strategic security and business objectives in an agile setting.