Experience
8 - 13 yrs
Salary (CTC)
₹35.3L - ₹39L
Job Location
Chennai, India
Vacancy
1
Designation
Architect
Job Type
Not specified
Job Description
Key Responsibilities
- Lead and mentor a small team of DevOps, MLOps, and cloud engineers; promote high performance, knowledge sharing, and continuous learning
- Architect and maintain scalable GCP infrastructure for AI agentic workloads (Vertex AI, GKE, Cloud Run, Cloud Functions, Artifact Registry, etc)
- Own DevOps and MLOps pipelines, including:
CI/CD with Google Cloud Build (primary) and integration/experience with Jenkins for hybrid/legacy workflows - Artifact management using Artifact Registry (primary) with exposure to Nexus Repository for proxying or migration scenarios
- IaC (Terraform preferred), container orchestration, and Python scripting/automation for custom tooling, Glue jobs, or pipeline extensions
- Lead API endpoint security strategy, leveraging:
Apigee for enterprise-grade API management, policy enforcement, quota, monetization (if applicable), advanced security, and analytics - Complementary GCP-native tools: Identity-Aware Proxy (IAP), Cloud Armor (WAF/DDoS), IAM least-privilege, OAuth 2
- 0/JWT/mTLS, Secret Manager, VPC Service Controls
- Zero-trust/BeyondCorp principles and threat protection for AI agent communications and customer-facing APIs
Champion FinOps practices on GCP:
Implement cost monitoring (Cloud Billing, FinOps Hub), optimization recommendations (Recommender, Active Assist), commitment-based discounts (CUDs), budget alerts, and idle resource cleanup.
Drive cost allocation, forecasting, and cross-team accountability for high-cost AI workloads (e.g., model training/inference).
Collaborate with AI/ML engineers to productionize agentic workflows with secure, governed access to models/data.
Ensure high observability (Cloud Operations Suite, Prometheus/Grafana), resilience, and SRE practices (incident response, post-mortems).
Establish cloud governance, compliance, and disaster recovery aligned with business needs.
Implement cost monitoring (Cloud Billing, FinOps Hub), optimization recommendations (Recommender, Active Assist), commitment-based discounts (CUDs), budget alerts, and idle resource cleanup.
Drive cost allocation, forecasting, and cross-team accountability for high-cost AI workloads (e.g., model training/inference).
Collaborate with AI/ML engineers to productionize agentic workflows with secure, governed access to models/data.
Ensure high observability (Cloud Operations Suite, Prometheus/Grafana), resilience, and SRE practices (incident response, post-mortems).
Establish cloud governance, compliance, and disaster recovery aligned with business needs.
Required Qualifications & Experience
8+ years in DevOps, cloud engineering, or infrastructure roles, with 4+ years deep hands-on Google Cloud Platform (GCP).
Proven people leadership (5+ reports) in agile/fast-paced environments.
Strong expertise securing APIs/services on GCP, preferably with Apigee (enterprise API management, policies, analytics) alongside IAP, Cloud Armor, IAM, and mTLS.
8+ years in DevOps, cloud engineering, or infrastructure roles, with 4+ years deep hands-on Google Cloud Platform (GCP).
Proven people leadership (5+ reports) in agile/fast-paced environments.
Strong expertise securing APIs/services on GCP, preferably with Apigee (enterprise API management, policies, analytics) alongside IAP, Cloud Armor, IAM, and mTLS.
Hands-on experience with:
CI/CD: Google Cloud Build + integration with Jenkins.
Artifact management: Artifact Registry + familiarity with Nexus Repository.
GKE/Cloud Run, monitoring/logging.
Python for automation, scripting, and tooling in DevOps/MLOps contexts.
Solid understanding of networking (VPC, Private Service Connect), security, and compliance.
Experience with AI/ML platforms (Vertex AI, Agent Builder) and MLOps for agentic systems is highly desirable.
CI/CD: Google Cloud Build + integration with Jenkins.
Artifact management: Artifact Registry + familiarity with Nexus Repository.
GKE/Cloud Run, monitoring/logging.
Python for automation, scripting, and tooling in DevOps/MLOps contexts.
Solid understanding of networking (VPC, Private Service Connect), security, and compliance.
Experience with AI/ML platforms (Vertex AI, Agent Builder) and MLOps for agentic systems is highly desirable.
Preferred Skills
Google Cloud certifications: Professional DevOps Engineer, Cloud Architect, Cloud Security Engineer (Apigee-related knowledge a plus).
FinOps experience or certification; familiarity with GCP FinOps Hub, Recommender, and commitment management.
Exposure to agentic AI patterns (multi-agent orchestration, RAG) and their infra requirements.
Experience in high-security or regulated environments.
Google Cloud certifications: Professional DevOps Engineer, Cloud Architect, Cloud Security Engineer (Apigee-related knowledge a plus).
FinOps experience or certification; familiarity with GCP FinOps Hub, Recommender, and commitment management.
Exposure to agentic AI patterns (multi-agent orchestration, RAG) and their infra requirements.
Experience in high-security or regulated environments.
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