DevSecOps Engineer AI/ML PlatformsSragvin Data Technologies Llp • New York, New York, United States
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DevSecOps Engineer AI/ML Platforms
Sragvin Data Technologies Llp
- New York, New York, United States
- New York, New York, United States
Über
Securing our MLOps pipelines against emerging threats, and (2)
Building specialized AI Agents that automate our internal operations.
You will act as the bridge between Data Science and Platform Engineering, ensuring our AI models are not only secure but that we are actively using AI to improve our own infrastructure.
Key Responsibilities 1. Agentic Automation (The Builder Role)
Build Operations Agents: Develop intelligent agents using
Vertex AI Agent Builder ,
LangChain , and
Python .
Infrastructure Interaction: Design "Function Calling" capabilities that allow Gemini models to securely interact with our infrastructure (e.g., Agent, check why this pod crashed and fetch the logs).
RAG Implementation: Build Retrieval-Augmented Generation pipelines to ground agents in our internal runbooks and architecture documentation.
2. AI & MLOps Pipeline Security
Secure the Supply Chain: Architect hardened MLOps pipelines using
Vertex AI and
Kubeflow , ensuring strict chain-of-custody for training data and model artifacts.
LLM Guardrails: Implement security controls for Generative AI endpoints to prevent Prompt Injection, Jailbreaking, and PII leakage (using tools like NVIDIA NeMo or custom GCP logic).
3. GCP Infrastructure & Governance
Infrastructure as Code: Manage ephemeral training environments and persistent inference clusters ( GKE Autopilot ) using
Terraform .
Policy & Isolation: Implement VPC Service Controls and Organization Policies to create security perimeters around sensitive BigQuery datasets.
ML-Specific CI/CD: Build pipelines (Cloud Build/GitHub Actions) that strictly automate model evaluation and bias detection before deployment.
4. Security Operations (SecOps)
Vulnerability Management: Integrate container scanning (Artifact Registry) and SAST/DAST into the ML workflow.
Identity Architecture: Design "Least Privilege" access models for both humans and AI agents using
Workload Identity Federation .
Technical Requirements
Cloud Platform: 4+ years of hands‑on experience with
Google Cloud Platform (GCP), specifically
Vertex AI ,
GKE ,
BigQuery , and
IAM .
AI Development: Strong proficiency in
Python with experience building agents/apps using
LangChain or
Vertex AI APIs .
DevOps Tooling: Expert-level
Terraform skills and proficiency with
GitHub Actions .
Containerization: Deep understanding of Docker and Kubernetes (including GPU resource management).
Nice‑to‑Have
Experience with Vector Databases (Pinecone, Vertex AI Vector Search).
Knowledge of Private Service Connect for isolating AI endpoints.
Certification: Google Professional Cloud Architect or Machine Learning Engineer.
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Sprachkenntnisse
- English
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