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DeepTempo

DevSecOps Engineer - LogLMs at scale

DeepTempo, Menlo Park, California, United States, 94029

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DeepTempo is pioneering the use of foundation models in cybersecurity, delivering market-leading accuracy and adaptability across network, cloud, and application domains. Our LogLM™ technology processes massive streams of logs in near-real time, surfacing novel threats that traditional defenses miss with 1% or lower false positives. We are being used by a number of the largest enterprises, service providers, and governments to see and respond to attacks with lower MTTD and with greater accuracy. As we scale, we're seeking an experienced DevSecOps Engineer to strengthen our infrastructure capabilities and security posture while supporting our AI/ML workflows.

Role Overview As a Lead DevSecOps Engineer, you will:

Design, implement, and maintain secure, scalable infrastructure using Infrastructure as Code principles

Build and enhance our CI/CD pipelines with security controls and observability built-in

Develop comprehensive monitoring and observability solutions for both applications and infrastructure

Support MLOps workflows and AI model deployment pipelines

Collaborate with engineering teams to embed security practices throughout the development lifecycle

Drive automation initiatives that improve reliability, security, and operational efficiency

Key Responsibilities

Infrastructure as Code

: Design and implement infrastructure using Terraform, CloudFormation, or similar tools, ensuring repeatability and version control

Observability & Monitoring

: Build comprehensive monitoring, logging, and alerting systems for applications, infrastructure, and ML models using tools like Prometheus, Grafana, ELK stack, or cloud-native solutions

MLOps Support

: Design and maintain deployment pipelines for AI/ML models, including model versioning, A/B testing infrastructure, and performance monitoring

Platform Engineering

: Develop and maintain internal developer platforms and tooling that enable engineering teams to deploy securely and efficiently

Incident Response

: Participate in on-call rotations, troubleshoot production issues, and implement improvements to prevent future incidents

Cloud Architecture

: Design and optimize cloud infrastructure across AWS, Azure, or GCP with a focus on security, cost-efficiency, and performance

Automation

: Build automation tools and scripts to reduce manual operational overhead and improve system reliability

Minimum Qualifications

DevSecOps Experience

: 4+ years hands-on experience in DevOps, Platform Engineering, or Site Reliability Engineering with a strong security focus

Infrastructure as Code

: Proven experience with Terraform, CloudFormation, Pulumi, or similar IaC tools in production environments

Observability

: Deep experience implementing monitoring, logging, and alerting solutions using modern observability stacks

Cloud Platforms

: Strong experience with at least one major cloud provider (AWS, Azure, GCP) including security best practices

CI/CD

: Experience building and maintaining CI/CD pipelines with integrated security scanning and automated testing

Container Technologies

: Proficiency with Docker, Kubernetes, and container orchestration in production environments

Programming

: Solid scripting and programming skills in Python, Go, Bash, or similar languages

Security Mindset

: Understanding of security principles and secure coding practices

Preferred Qualifications

Experience with MLOps workflows, model deployment pipelines, and ML infrastructure (MLflow, Kubeflow, SageMaker, etc.)

Experience with scalable data processing workloads and technologies such as Apache Spark, Flink, Kafka, and RedPanda.

Deep experience with AWS.

Familiarity with AI agent coding tools and workflows

Experience with service mesh technologies and zero-trust networking

Knowledge of compliance frameworks (SOC 2, FedRAMP, GDPR) and implementation

Experience with log analysis platforms and security information systems

Contributions to open-source DevOps or security tools

Experience with federated learning or distributed ML systems

What We Value

Curiosity

: You ask the hard questions, dig into root causes, and relentlessly seek better solutions

Collaboration

: You thrive in cross-functional teams and elevate teammates through clear communication

Ownership

: You treat the business as your own, seeing projects through and iterating until they succeed

Adaptability

: You excel in a fast-moving environment, pivoting priorities as market and customer needs evolve

We are increasing in value rapidly - and offer attractive equity packages.

Seniority level Mid-Senior level

Employment type Full-time

Job function

Engineering and Information Technology

Industries

Computer and Network Security

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