AI Infrastructure Engineer

Cloud Infrastructure & Agentic Systems · A4I (IIIT-B × Microsoft)

Must-Have Skills: Azure | Kubernetes | Docker | Terraform

Organization: A4I (IIIT-B × Microsoft)

Location: IIIT Bangalore

Type: Contract (through March 2027, extendable)

Experience: 4 to 7 years About A4I

AI Innovation and Inclusion Initiative (A4I) is a partnership between Microsoft and IIIT-B, collaborating with non-profit partners to harness AI for solving real-world challenges in education, healthcare, accessibility, and agriculture. A4I evolves innovations into open-source Digital Public Goods (DPGs) that are deployable at scale, fostering a strong AI innovation ecosystem for social impact. The Opportunity

Imagine infrastructure that doesn't just run AI — it enables it. At A4I, we're not spinning up VMs and hoping for the best. We're engineering the cloud backbone that powers autonomous multi-agent systems serving millions of users in India's most underserved communities: students learning in vernacular languages, frontline health workers operating without connectivity, farmers acting on real-time data.

Generic DevOps practices don't work here. The constraints are too real — cost ceilings, data sovereignty, multilingual AI services, and uptime requirements that leave no room for reactive firefighting. What we need is an infrastructure engineer who has already shipped cloud systems that carry production AI workloads, and who treats automation as a discipline, not an afterthought.

This isn't a role for someone who manages tickets. It's for someone ready to own the platform, drive infrastructure decisions, and build the foundation that makes A4I's Digital Public Goods not just functional — but scalable, secure, and sustainable.

What You Will Be

Doing

Building and Owning Cloud Infrastructure

- Design, provision, and manage multi-application cloud environments on Azure (primary), with working knowledge of AWS and GCP — across compute, networking, storage, and managed services.
- Own environment parity between staging and production for a portfolio of live agentic AI applications, ensuring consistent, reproducible deployments.
- Drive Infrastructure as Code (IaC) practices using Terraform and Ansible, eliminating manual provisioning and enabling GitOps workflows.

Enabling Reliable AI Workloads in Production

- Deploy and manage containerised AI services using Docker and Kubernetes (AKS), including LLM inference endpoints, vector store services, and RAG pipeline backends.
- Maintain CI/CD pipelines (GitHub Actions or Azure DevOps) for AI and web application workloads — with staged rollouts, automated testing gates, and rollback capabilities.
- Implement and evolve MLOps tooling: model versioning,



inference monitoring, and deployment pipelines that support safe, auditable releases into high-stakes social contexts.

Platform Resilience and Disaster Recovery

- Design and implement backup and restoration strategies across heterogeneous data stores — PostgreSQL, MongoDB Atlas, CosmosDB, and vector databases — with tested recovery procedures.
- Architect and maintain disaster recovery (DR) plans covering multi-region failover, RTO/RPO targets, and regular DR drills for all production systems.
- Manage Kubernetes cluster resilience: node auto-scaling, pod disruption budgets, health checks, and stateful workload recovery.

Observability and Security

- Build and maintain the end-to-end observability stack — Azure Monitor, Grafana, Prometheus, and ELK/OpenSearch — with dashboards, alerting, and incident runbooks for all A4I applications.
- Enforce security best practices across the platform: Entra ID access control, secrets management, encryption at rest and in transit, and PI data handling in line with annual audit requirements.
- Manage Cloudflare for CDN, DNS routing, and edge security across all A4I-hosted applications.

Cost Governance and Platform Strategy

- Own cloud cost visibility and optimisation — right-sizing, reserved capacity planning, and rationalisation across multi-application Azure subscriptions.
- Evaluate and recommend cloud provider and tooling decisions, balancing cost, open-source compliance, and DPG deployment requirements.
- Automate operational workflows using Python and Bash, moving the team from reactive support to proactive, self-healing infrastructure.

What You Will Not Be

Doing

- Managing tickets and waiting for engineers to raise infrastructure requests — this is a proactive ownership role.
- Working on a single-application setting — you will own a portfolio of distinct AI products simultaneously.
- Operating in isolation — you will collaborate directly with AI architects, engineers, and domain partner teams across NGOs and research institutions.
- Maintaining legacy on-premise infrastructure — the entire stack is cloud-native.

Must-Have Skills

Each bullet represents a skill area we will probe directly during screening.

- Cloud platform ownership: Hands-on production experience with Azure, including App Services, Function Apps, AKS, Container Registry, CosmosDB, and Entra ID.



Familiarity with native VMs is required, AWS and GCP experience is optional.
- Infrastructure as Code: Proficiency with Terraform and/or Ansible for managing multi-environment cloud infrastructure; you write IaC, you don't patch portals.
- Kubernetes and containers: Real production experience with Docker and Kubernetes (AKS or equivalent) — including stateful workload management, cluster scaling, and service mesh basics.
- Backup, restoration, and DR: Demonstrated experience designing and testing backup strategies and disaster recovery plans across cloud-native databases and stateful services.
- CI/CD pipelines: Proven track record building and maintaining CI/CD pipelines for containerised AI/ML and web workloads using GitHub Actions, Azure DevOps, or equivalent.
- Observability: Hands-on experience with Azure Monitor, Grafana, Prometheus, and ELK/OpenSearch for production monitoring, alerting, and incident response.
- Security and compliance: Working knowledge of IAM, Entra ID, secrets management (Key Vault or equivalent), encryption practices, and PI data handling requirements.
- Python and Bash: Production-quality scripting for automation, infrastructure tooling, and operational workflows — not just glue scripts. Qualifications
- Bachelor's or Master's in Computer Science, Engineering, or equivalent demonstrated expertise.
- 4 to 7 years in cloud infrastructure, platform engineering, or DevOps roles, with at least 2 years supporting live AI or ML workloads in production.
- Demonstrated experience managing infrastructure for more than one live application simultaneously.
- Verifiable track record of cloud cost governance and right-sizing in real production environments — references or case studies will be requested.

Preferred

Qualifications

- Hands-on experience with Azure-specific AI services: Azure OpenAI, Cognitive Services, Bhashini integration, or Sarvam AI endpoints in production.
- Familiarity with vector databases (pgvector, Qdrant, or equivalent) and the infrastructure considerations for embedding pipelines at scale.
- Prior work in open-source DPG deployments or multi-tenant cloud environments serving low-resource or low-connectivity end users.
- Experience with Cloudflare Workers, DNS management, and edge security configuration.
- Contributions to open-source infrastructure tooling, IaC libraries, or internal platform frameworks.
- Exposure to multilingual AI service infrastructure — Indic language models, ASR/TTS endpoints, or translation API pipelines. A4I is committed to building AI systems that serve everyone. We actively encourage applications from candidates of all backgrounds, disciplines, and geographies.

📌 AI Infrastructure Engineer (Bengaluru)
🏢 AI Innovation and Inclusion Initiative (A4I)
📍 Bengaluru