Hi, I’m Serdar İlder Çağlar

AI/ML Engineering Lead & Solutions Architect with a passion for building production-grade artificial intelligence systems that solve real-world problems.

What I Do

I specialize in architecting and implementing enterprise-scale AI solutions, with a particular focus on:

🤖 Retrieval-Augmented Generation (RAG) Systems

  • Production-ready RAG implementations
  • Advanced retrieval strategies and optimization
  • Multi-modal and agentic RAG patterns
  • Evaluation frameworks and MLOps integration

🚀 ML Engineering & Operations

  • End-to-end ML pipeline design and implementation
  • Model deployment and serving at scale
  • ML infrastructure and platform engineering
  • Performance optimization and monitoring

🏗️ AI Solutions Architecture

  • System design for AI-powered applications
  • Integration strategies for existing enterprise systems
  • Scalability planning and technical decision making
  • Cross-functional team leadership and technical guidance

Technical Expertise

Languages & Frameworks:

  • Python, SQL, JavaScript
  • PyTorch, TensorFlow, Hugging Face Transformers
  • LangChain, LlamaIndex, Haystack
  • FastAPI, Flask, Streamlit

ML & AI Platforms:

  • AWS SageMaker, Google Cloud AI Platform
  • MLflow, Weights & Biases, Neptune
  • Docker, Kubernetes, Terraform
  • Vector databases (Pinecone, Weaviate, Chroma)

Data Engineering:

  • Apache Airflow, Spark, Kafka
  • PostgreSQL, MongoDB, Redis
  • ETL/ELT pipeline design
  • Real-time data processing

Recent Focus

Currently, I’m deep into:

  • Advanced RAG Architectures: Exploring cutting-edge patterns for enterprise RAG systems
  • LLM Operations: Building robust production pipelines for large language model applications
  • AI Agent Frameworks: Developing multi-agent systems for complex workflow automation

Let’s Connect

I’m always interested in discussing:

  • AI/ML engineering challenges and solutions
  • Production ML system design
  • RAG implementation strategies
  • Technical leadership in AI teams

Get in touch:


“Building AI systems that not only work in the lab, but thrive in production.”