Machine Learning Engineer/GenAI

Orbina; Turkey; Hybrid; TRY 250,000 - 500,000; Full time

Job summary

Orbina is seeking a Senior AI/ML Engineer to design, build, and optimize AI systems with emphasis on Retrieval-Augmented Generation, on-prem deployments, and agentic workflows. You’ll shape our AI strategy and deliver production-ready ML pipelines across diverse domains.

The role requires 5+ years of production AI/ML experience, deep Python expertise, and hands-on with LLMs, vector databases, and MLOps in a hybrid environment.

Qualifications

• 5+ years of experience building and deploying AI/ML systems in production environments.
• Proficiency in Python and frameworks such as PyTorch, TensorFlow, or scikit-learn.
• Deep knowledge of LLMs, vector embeddings, prompt engineering, and RAG system design.

Responsibilities

• Design, develop, and optimize AI/ML solutions with a focus on LLMs and RAG architectures.
• Fine-tune LLMs for domain-specific use cases and improve model accuracy and robustness.
• Build agentic workflows and multi-step reasoning pipelines for autonomous tasks.
• Architect and deploy on-premise and hybrid AI infrastructures (GPU clusters, inference servers).
• Optimize model inference for low latency and high throughput.
• Develop compound AI systems with multiple models and agents in coordination.
• Build orchestration flows using LangChain or similar frameworks.
• Implement continuous learning from user interactions and feedback.

Skills

Python

LLMs

RAG

On-prem deployment

MLOps

SQL

Git

LangChain

Tools

Milvus

Weaviate

vLLM

GPU clusters

Job description

Orbina is a platform development company rooted in generative artificial intelligence. Our mission is to leverage advanced technology to make the world a better place through innovative, high-impact AI solutions. We are dedicated to enhancing the user experience of every product we build, while creating scalable, intelligent systems that drive real value.

Exceptional technology is built by exceptional individuals. We are seeking a Senior AI/ML Engineer with deep expertise in modern AI systems—someone who can architect, build, and optimize advanced machine learning pipelines, particularly around RAG frameworks, on-prem deployments, and agentic workflows. In this role, you will help shape our AI strategy, influence technical decisions, and deliver production-ready solutions across diverse business domains.

Responsibilities

• Design, develop, and optimize AI/ML solutions with a strong focus on Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) architectures.
• Fine-tune LLMs for domain-specific use cases and continuously improve model accuracy, robustness, and performance.
• Build agentic workflows, multi-step reasoning pipelines, and specialized autonomous agents for tasks such as SQL generation, explanation, validation, and visualization.
• Architect and deploy on-premise and hybrid AI infrastructures, including GPU clusters, inference servers, caching layers, and monitoring systems.
• Optimize model inference for low latency, high throughput, and efficient resource utilization.
• Develop compound AI systems leveraging multiple models and agents working in coordination.
• Build and maintain orchestration flows using LangChain or similar frameworks.
• Implement continuous learning mechanisms driven by user interactions and feedback loops.
• Collaborate with cross-functional teams and contribute to architectural decisions, best practices, and long-term AI strategy.

Required Qualifications & Skills

• 5+ years of experience building and deploying AI/ML systems in production environments.
• Strong proficiency in Python and experience with frameworks such as PyTorch, TensorFlow, or scikit-learn.
• Deep knowledge of LLMs, vector embeddings, prompt engineering, and RAG system design.
• Proven experience building on-prem AI or RAG infrastructures, including GPU inference servers, vLLM, and distributed serving setups.
• Expertise with vector databases such as Milvus, Weaviate, or pgvector.
• Strong SQL skills and familiarity with enterprise data environments.
• Hands-on experience deploying AI systems on AWS, Azure, GCP, or hybrid environments.
• Solid understanding of MLOps practices: CI/CD, model monitoring, observability, versioning, and lifecycle management.
• Strong understanding of data privacy, security, and compliance considerations in AI development.
• Proficiency with Git and collaborative development workflows.

Preferred Qualifications

• Experience with LangChain, LlamaIndex, or other orchestration frameworks.
• Familiarity with high-performance inference tools such as vLLM.
• Deep knowledge of vector database internals and advanced features.
• Experience building rapid prototypes or data applications with Streamlit.
• Familiarity with visualization tools such as Plotly or Altair.
• Experience delivering solutions to specific industries and applying domain-driven design to AI workflows.
• Experience with ensemble or compound AI architectures and multi-agent systems.
• Knowledge of Docker and container-based deployment workflows.