Job Summary
Job Title: Senior DevSecOps AI Engineer (GCP & Agentic Automation) Location: [INDIA Hyderabad] Type: Contractor Experience: 4-5 years Role Overview: We are seeking a hands-on DevSecOps Engineer to architect our next-generation AI platforms on Google Cloud. This role helps us move beyond traditional CI/CD by satisfying two critical needs: (1) 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 (Pineco
Key Responsibilities
1. Implement and optimize ML pipelines using MLflow, Kubeflow Pipelines, and TFX, enabling automated model training, validation, and deployment.
2. Integrate DevOps practices with Python scripting to automate infrastructure provisioning via Terraform, AWS CloudFormation, and Ansible for scalable ML environments.
3. Configure and maintain CI/CD workflows using Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions to streamline code integration and deployment for ML projects.
4. Monitor and analyze ML system performance using Prometheus, Grafana, ELK Stack, and Fluentd, ensuring reliability and rapid issue resolution.
5. Apply advanced proficiency in Git, GitHub, GitLab, and Bitbucket for source code management and collaboration within the development team.
6. Participate in technical reviews, contribute to process compliance, and support feasibility studies by evaluating technical alternatives and risks for ML solutions.
7. Prepare and submit project status reports, collaborating with internal stakeholders to define deliverables and minimize escalation risks.
Skill Requirements
Job Title: Senior DevSecOps AI Engineer (GCP & Agentic Automation) Location: [INDIA Hyderabad] Type: Contractor Experience: 4-5 years Role Overview: We are seeking a hands-on DevSecOps Engineer to architect our next-generation AI platforms on Google Cloud. This role helps us move beyond traditional CI/CD by satisfying two critical needs: (1) 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 (Pineco
Other Requirements
Job Title: Senior DevSecOps AI Engineer (GCP & Agentic Automation) Location: [INDIA Hyderabad] Type: Contractor Experience: 4-5 years Role Overview: We are seeking a hands-on DevSecOps Engineer to architect our next-generation AI platforms on Google Cloud. This role helps us move beyond traditional CI/CD by satisfying two critical needs: (1) 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 (Pineco