AI Platform & DevSecops Engineer

Company Overview

Trianz is an applied AI solutions company that accelerates customer business transformation through AI powered Transformation Services as a Software Model. With 25+ years of transforming enterprises, we've evolved to a product-led, platform-driven organization serving global enterprises across Financial Services, Insurance, Healthcare, Hi-Tech, Manufacturing, and other industries.

With global presence across 4 continents, our platform portfolio under the unified Concierto brand delivers end-to-end transformations including solutions for Migrate, Manage, Maximize, Modernize, Insights & Agentic AI, and SecOps - delivered through strategic partnerships with leading hyperscalers.

We're building the premier innovation-led organization in the digital transformation space through AI-first methodologies and data-driven excellence - RevolutionAIzing Transformations.

Role Overview

You execute the AI platform architecture designed by the Principal AI Architect. Your primary metric is deployment speed and reliability — taking AI system deployments from hours to minutes through automation, pipeline engineering, and self-service tooling. You build the CI/CD pipelines for model releases, provision GPU and CPU compute across multiple clouds and on-premises environments, and ensure every deployment is security-scanned and quality-gated. Fully hands-on, no coordination work.

Key Responsibilities

• Build CI/CD pipelines for LLM model releases: version control, staged rollout, automated rollback
• Write infrastructure-as-code (Terraform or Pulumi) for GPU and CPU node provisioning across AWS, Azure, GCP, and on-premises OpenShift
• Build self-service deployment automation — scripts that spin up a complete AI serving environment in minutes
• Implement DevSecOps pipeline gates: container image scanning, IaC scanning, secrets detection, network policy enforcement
• Manage GPU node provisioning: CUDA driver management, node labelling, GPU resource allocation in Kubernetes
• Build monitoring and alerting for serving infrastructure: model latency, throughput, error rates, GPU utilisation
• Automate OpenShift operator deployments for on-premises environments
• Implement blue-green deployment and automated rollback for model releases

Ideal Candidate Experience

Exp: 7+ years

Must Have

• Built CI/CD pipelines that shipped AI or ML models to production — not just application code
• Hands-on with Terraform or Pulumi at production scale across at least two cloud providers
• Provisioned GPU nodes in Kubernetes: NVIDIA device plugin, node selectors, resource limits
• DevSecOps tooling in practice: Trivy, Checkov, Vault, OPA, or equivalent
• Linux scripting (Bash/Python) — production-grade automation without supervision
• Experience with RedHat OpenShift operator deployment in on-premises environments
• 4+ years in DevOps with at least 2 years in an AI or ML platform context

Nice To Have

• NVIDIA MIG partitioning for multi-tenant inference
• Helm chart authoring for ML serving workloads
• GitOps tools: ArgoCD or Flux
• GPU compute cost optimisation: spot instances, reserved capacity, rightsizing

Why Join Trianz

Architectural Impact: Own the complete private AI infrastructure for Fortune 500 enterprises. Your decisions influence how LLMs run in the most security-conscious, regulated environments globally.

Technical Excellence: Work with cutting-edge inference frameworks, open-source models, and heterogeneous hardware. Design systems that work across AWS, Azure, GCP, on-prem, and air-gapped environments — not just API consumption.

Sovereign AI Leadership: Lead the charge in sovereign AI deployment. Help enterprises reclaim control of their AI infrastructure while maintaining the flexibility to scale globally.

Zero Bureaucracy: Pure IC role with architectural authority. No slow approval cycles — your design decisions move fast into production.

Enterprise Scale: Work on transformations across Fortune 500 organizations. See your architecture deployed across continents, industries, and mission-critical use cases.

Growth Through Ambiguity: Thrive in the emerging sovereign AI space where problems are complex, standards are evolving, and your expertise will define the industry.