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Part-Time CourseLLMOps Infrastructure Engineering
Build production-ready AI systems over 5 weeks. This part-time program teaches you how to deploy and operate Large Language Model applications in production. Build a complete LLM-powered system with vector databases, monitoring, and operational intelligence, skills that are essential for the AI-driven future.
Part-Time
weeks
Remote
English
Program overview
Our LLMOps Infrastructure Engineering program takes you from data fundamentals to production AI systems over 5 weeks. Master embeddings, vector databases, and RAG architectures, then dive into LLM APIs, FastAPI services, and monitoring with LangFuse and Prometheus. The program culminates in building operational intelligence systems that automate insights from logs and incidents. With live sessions and expert support, you'll gain hands-on experience throughout. Check the upcoming dates and join us!
Upcoming dates
Course dates
Jun 10 - Jul 11
Apply by
Sep 08
Tuition
3'500 EUR
Format
Remote
Course dates
Sep 09 - Oct 10
Apply by
Sep 08
Tuition
3'500 EUR
Format
Remote
Course dates
Nov 18 - Dec 19
Apply by
Nov 17
Tuition
3'500 EUR
Format
Remote
Schedule doesn't fit your needs?
Check out our remote options or the Full-Time program.
Looking for financing? Check out our financing options.
Schedule
- Wed
Remote
- 18.00 - 21.00Lecture
- Fri
Remote
- 18.00 - 21.00Practice
- Sat
Remote
- 09.00 - 12.00Lecture
- 13.00 - 16.00Practice
LectureLearn from our instructors who are experts in their respective fields and get introduced to new topics during live lectures.
PracticeWork on a set of interesting and challenging exercises related to the topics covered during the previous lecture. Practice your team-building skills by doing group projects together with your peers.
Thi Tuyen Nguyen
Data Science
❝The intense curriculum of the bootcamp pushed me outside my comfort zone, enhancing my resilience and passion for continuous learning while equipping me with essential skills for a transformative career in data science.❞
Before: Postdoctoral Researcher
After: Artificial Intelligence Intern at Baader Bank AG
What you will learn
Preparation
Prerequisites
- Python Basics: Functions, loops, dictionaries, and command-line fundamentals.
- Math Literacy: Basic math foundations for understanding AI concepts.
- DevOps Knowledge: Basic DevOps stack knowledge recommended.
Day 1
Data Fundamentals
- Pandas: Work with operational data using tables, text, and logs.
- Data Formats: Handle CSV, JSON, and API responses.
- Data Quality: Clean and validate data for reliable AI processing.
Day 2
Statistics and Text Processing
- Distributions: Analyze data distributions to ensure quality inputs.
- Tokenization: Understand tokenization and its impact on cost and performance.
- Time Series: Work with time-series data and detect changes over time.
Day 3
Embeddings and Vector Databases
- Embeddings: Understand how AI captures meaning in numbers.
- Vector Databases: Compare vector databases to traditional SQL databases.
- Semantic Search: Build semantic search with nearest-neighbor retrieval.
Day 4
From Machine Learning to LLMs
- Classical ML: Understand classical machine learning and its limitations.
- LLM Architecture: Learn how LLMs work with tokens, embeddings, and transformers.
- Failure Modes: Recognize hallucinations and reasoning gaps in LLMs.
Day 5
Reproducible Data Pipelines
- Data Pipelines: Create deterministic pipelines from data ingestion to storage.
- Dataset Preparation: Prepare datasets for retrieval, prompts, and evaluation.
- Pipeline Validation: Validate pipeline consistency for production reliability.
Day 6
LLM APIs and Production Basics
- LLM APIs: Work with OpenAI, Anthropic, and Hugging Face APIs.
- Production Challenges: Handle rate limits, costs, and error recovery.
- Quality Tracking: Track API calls and monitor quality with logging.
Day 7
Building LLM Services
- FastAPI: Build async APIs for high-performance AI services.
- Redis: Manage conversation state with in-memory data stores.
- Session Management: Handle sessions and context for multi-turn conversations.
Day 8
RAG Systems and Deployment
- RAG Architecture: Build retrieval-augmented generation with embeddings, vector DB, and LLM.
- Database Integration: Integrate PostgreSQL, Redis, and vector stores.
- Docker Deployment: Deploy AI services with Docker and Docker Compose.
Day 9
Monitoring and Observability
- Observability: Implement monitoring with LangFuse and OpenTelemetry.
- Drift Detection: Detect data drift, embedding drift, and concept drift.
- Dashboards: Build dashboards with Prometheus and Grafana.
Day 10
Operational Intelligence
- Automated Insights: Generate insights automatically from logs and incidents.
- Alert Clustering: Cluster alerts and detect anomalies with embeddings.
- Predictive Dashboards: Build intelligent dashboards with predictive capabilities.
Application process
Apply to the program hereInformative call with Constructor Nexademy (optional)
Pay a deposit to secure your spot
Complete your preparation work before the program starts (beginners only)
Start learning! 😊
Get ready for the course
Free Data Science intro course
Online
Self-paced
Free of charge
Learn about Python, the data science project lifecycle, and practice on a real-world data science problem in this free self-paced online tutorial. By completing this course, you will gain a better understanding of the Data Science world and increase your chances of being accepted into the Intensive program.
Estimated time to complete: 15 hours
Topics
Data Science for LLMs
Master data preparation and processing for AI systems. Work with Pandas for operational data, understand embeddings and vector similarity, and build reproducible data pipelines for production AI.
LLM Technologies
Work with cutting-edge LLM APIs from OpenAI, Anthropic, and Hugging Face. Build RAG architectures that combine embeddings, vector databases, and LLM generation for intelligent retrieval systems.
Infrastructure & Operations
Deploy production AI systems with FastAPI, Docker, and Docker Compose. Integrate PostgreSQL, Redis, and vector databases. Monitor with LangFuse, OpenTelemetry, Prometheus, and Grafana.
LLMOps Infrastructure Engineering
This intensive program teaches you how to deploy and operate Large Language Model applications in production. You'll master data pipelines, vector databases, and RAG architectures, then deploy complete AI systems with monitoring and observability. By the end, you'll have built a complete LLM-powered system with automated pipelines and operational intelligence, skills that are essential for the AI-driven future.
- Data pipelines, embeddings, vector databases, and RAG architecture
- A solid foundation for LLMOps and AI Infrastructure Engineering roles
- Ideal for DevOps engineers, SREs, and platform engineers looking to specialize in AI infrastructure
- Hands-on learning through real-world AI projects
Over 60 hrs
hands on learning
Deploy production AI systems with monitoring and operational intelligence
Choose your location
Join us from anywhere
Take our life-changing courses from anywhere in the world and gain knowledge and skills without the need to travel. With our remote courses, you can learn from anywhere, with just a laptop!
Contact usFinancing options
At Constructor Nexademy, we believe that finances should never be a barrier to accessing the education and training that can help individuals achieve their goals. That's why we offer a variety of financing options to make our courses more accessible to a diverse range of students. We also work with external organizations that provide financial assistance to those in need.
Certificate from top coding school
Get certified by Constructor Nexademy, one of the world's top coding academies. Share your certificate on social networks, CVs and more. Boost your career with the new skills that you gained.
Upcoming events
Attend one of our events. Discover our upcoming workshops, info sessions, final presentations and webinars on trending topics.
FAQs
What other type of support will I have during the course?
In addition to the instructor, there will be Teaching Assistants (TAs). They will attend class with you and be a vital resource of assistance. The other students in the classroom, with whom you’ll form a strong bond, will also be a valuable source of help and collaboration. At Constructor Academy, it’s all about teamwork. The success of one student translates to the success of all.
What are the technical pre-requisites for the course?
You wil need a laptop with good internet connection.
How many students are there per class?
To maintain a high level of interaction and instruction, each class has an average of 15-20 students.
Is the program fully remote?
The program is delivered fully online in live sessions via Zoom
What coding level do I need?
Python Fundamentals, and completing the Python and Ops Intro Course if you are a beginner. Motivation and drive are the most important factors.
Instructors
Dr. Finn Jost
Teaching Assistant & Instructor
Dr Finn Jost has over five years of experience working across mathematical physics, data science, and teaching. With a background in academic research and industry projects, he is enthusiastic about uncovering patterns in complexity and explaining them illustratively and driven by real applications. This interest naturally led him to the Data Science program at Constructor Nexademy and joining the team felt like a natural next step. This allows him to combine teaching, mentoring, and course development helping learners to translate concepts into intuition gaining confidence through hands-on experience for their career.
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