by TensorHub Technologies
Programs / AI Deployment Engineer
Infrastructure & Ops

AI Deployment Engineer

Take agentic and generative AI systems from a working notebook to reliable, scalable production infrastructure. CI/CD, containers, Kubernetes, cloud AI platforms, autoscaling, and high-availability architecture built specifically for LLM and agent workloads.

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Duration
12 Weeks
Commitment
10–12 hrs/wk
Format
Live + Self-paced
Level
Intermediate
Curriculum

From containerized agent to production platform

1

Packaging & CI/CD for AI Systems

Weeks 1–3
  • Containerizing agents and LLM services with Docker
  • CI/CD pipelines for model and prompt versioning
  • Environment management and secrets handling
  • Testing strategy for non-deterministic AI outputs
2

Cloud AI Platforms & Orchestration

Weeks 4–6
  • Kubernetes fundamentals for AI workloads
  • AWS Bedrock, Azure AI Foundry, Google Vertex AI
  • Model serving patterns: batch, streaming, real-time
  • Redis, PostgreSQL, and Kafka in the agent data path
3

Scaling, Security & High Availability

Weeks 7–9
  • Autoscaling for bursty, cost-sensitive AI traffic
  • Load balancing across multiple model providers
  • Security hardening: secrets, network policy, prompt-injection defenses
  • Disaster recovery & failover for agent services
4

Monitoring & Capstone Deployment

Weeks 10–12
  • Production monitoring with Grafana & OpenTelemetry
  • Cost tracking and optimization for LLM inference
  • Capstone: deploy a multi-service agent system to a live cloud environment
  • Runbook and on-call readiness documentation
Outcomes

What you'll be able to do

  • ✓Containerize and deploy agentic AI systems with production-grade CI/CD
  • ✓Architect for scale on AWS Bedrock, Azure AI Foundry, or Vertex AI
  • ✓Design high-availability, secure infrastructure for AI workloads
  • ✓Monitor cost, latency, and reliability in real time
  • ✓Own the full deployment lifecycle from build to on-call support
Real-World Use Cases

Built on enterprise scenarios

E-commerce

Autoscaled product-search agent

Kubernetes-deployed agent service handling seasonal traffic spikes with cost-aware model routing.

Energy & Utilities

Multi-region resilient copilot

High-availability deployment across regions with automated failover for a field-operations assistant.

Government

Secure, air-gapped agent deployment

Hardened, network-isolated deployment pattern meeting public-sector security requirements.

Job Market

Compensation & demand

Indicative ranges based on current MLOps/LLMOps hiring patterns — actual compensation varies by experience, company, and geography.

RoleIndia (Annual)Global (Annual)
AI / LLMOps Deployment Engineer₹18L – ₹55L$130K – $230K
Senior AI Platform Engineer₹40L – ₹95L$200K – $360K
AI Infrastructure / SRE Lead₹55L – ₹1.3Cr+$260K – $450K+

Highest demand from cloud-native enterprises, GenAI platform teams, and regulated industries needing on-prem or hybrid deployment expertise.

✓

Designed and delivered by a Claude Architect–certified Forward Deployed Engineer

Every deployment pattern taught here was used to get a real agentic system live and stable in a production enterprise environment.

Claude Architect Certified Former Forward Deployed Engineer Production Infrastructure Experience
FAQ

Common questions

Do I need prior DevOps experience?

Basic familiarity with cloud platforms and command-line tools is recommended. Prior Docker or Kubernetes exposure helps but is taught from fundamentals.

Which cloud platforms are covered?

AWS, Azure, and Google Cloud are all covered, with hands-on labs on AWS Bedrock, Azure AI Foundry, and Google Vertex AI.

Learn to ship AI systems that stay up.

Get the full syllabus and sample deployment architectures.

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