LLMOps Engineer Program
LLM Production Operations
Become the ops engineer who runs AI models reliably, observably and cost-efficiently in production.
Fills the critical LLMOps gap for engineers who already know MLOps. Deep coverage of model serving (vLLM/TGI), observability (LangSmith/Phoenix), A/B testing, eval pipelines, Kubernetes for AI and multi-region deployment. Capstone: design a production-ready LLM platform.
Quick Facts
Why This Program for Your Company
Talent Development
Grow your in-house teams; reduce vendor and outsourcing dependency
Fast Time-to-Value
Built for a 90-day pilot-to-production trajectory
Measurable ROI
Before/after capability report + KPI dashboard with tangible outcomes
AI Culture
AI adoption across all levels — from executive to engineer
Delivery Models
Choose the delivery format that fits your team
On-site
At your company location, closed group
Hybrid
Online + periodic in-person intensives
Fully Remote
Live remote + recordings + lab notebooks
Train-the-Trainer
Build in-house trainers — long-term scaling
Tailored to Your Company
Content is customized to your industry, regulatory framework, existing tech stack and target use cases. Labs run on your existing systems or sample datasets.
Lab Environment
Hands-on labs run on your company data (under NDA), isolated sandbox or sample dataset
Post-Training Support
30 days async support (Slack/Teams/Discord) + optional monthly follow-up sessions + code review support
Why Now? — Türkiye's Empty Market
DataExpert/Patika cover classic MLOps but no LLMOps coverage exists in Turkish. Enterprise AI teams are left alone with the production-readiness question.
About the Program
Target Teams
- DevOps and Platform Engineers
- SREs
- Cloud Architects
- MLOps engineers transitioning to LLM
Your Team's Outcomes
- Model serving with vLLM, TGI and TensorRT-LLM
- End-to-end observability with LangSmith, Phoenix, Helicone
- Integrate eval pipelines into CI/CD
- Reduce cost via semantic cache and model cascading
- Manage clusters with Kubernetes GPU operator and KAITO
Prerequisites
- Docker and Kubernetes experience
- Knowledge of a cloud platform (AWS/GCP/Azure)
- Intermediate Python
Trainings in this Program
12 modules / micro-trainings
- 01
MLOps → LLMOps Transition Principles
- 02
Model Serving (vLLM, TGI, TensorRT-LLM)
- 03
Bedrock / Vertex / Azure OpenAI Management
- 04
Observability (LangSmith, Phoenix, Helicone, Datadog LLM)
- 05
Eval Pipeline (CI/CD Integrated)
- 06
A/B Testing (Prompt & Model)
- 07
Cost & Token Optimization
- 08
Semantic Cache Strategies
- 09
Kubernetes for AI (GPU Operator, KAITO, Kueue)
- 10
Multi-Region & Failover Patterns
- 11
Incident Response & Rollback
- 12
Capstone: Production-Ready LLM Platform
Capstone Project
Multi-region, observable, eval-CI-integrated production LLM platform design + IaC code + runbook deliverable.
How We Work
From discovery to delivery and post-training follow-up
- 1
Discovery
Free 30min — team capability map, use case discovery, goal setting
- 2
Design
Custom curriculum, lab scenarios and delivery timeline for your use cases
- 3
Delivery
Live training + hands-on labs + capstone project + completion certificate
- 4
Follow-up
Capability report + 30-day support + optional monthly check-in sessions
Career Path
Positions you can target after this program
Tech Stack & Topics
Frequently Asked Questions
How do enrollment and participant selection work?
How is pricing structured?
Can the curriculum be customized for our use cases?
On-site or remote?
Is post-training support included?
Are certificates provided?
Who is this program for?
What will I learn?
What is the duration and format?
What are the prerequisites?
Which positions does this program prepare me for?
Why is this program needed in Türkiye?
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Bring This Program to Your Team
In a free 30-minute discovery call we map your team's capability, explore your target use cases and prepare a custom quote for your company. No commitment.