Orbina is an artificial intelligence-powered communication platform. It aims to make both customer interactions and internal processes of businesses more efficient and intelligent by utilising big…
Orbina is an artificial intelligence-powered communication platform. It aims to make both customer interactions and internal processes of businesses more efficient and intelligent by utilising big…
What they do: AI orchestration platform for customer experience and operations
Headquarters: Istanbul, Türkiye
Founded: 2022
Team size: 14 employees
Latest funding: Seed (announced Jun 25, 2025) led by ENA Venture Capital
Company Overview
Problem Domain
Customer experience (CX) automation and operational workflow automation using AI
Founded
2022
Industry
Enterprise software / Artificial intelligence
Funding Track Record
Seed- 2025-06-25
Investor Signal
“ENA Venture Capital (lead/sole investor in Seed round announced Jun 25, 2025)”
Founders
What we do
Join the Team
Senior AI/ML Engineer
RemoteTR
Remote • TR
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Senior AI/ML Engineer
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
Required Qualifications & Skills
5+ years
of experience building and deploying AI/ML systems in production environments.
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.
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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.
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.
Knowledge of Docker and container-based deployment workflows.
