Senior AI Engineer with RAG and Agentic architectures
- Hiring from
- Turkey
- Work type
- Remote
- Posted
- Sep 26, 2026
Is this job info correct?
Back to Search
Senior AI Engineer with RAG and Agentic architectures
Remote in Türkiye
AI Solution Engineering& 6 others
apply
Looking for something else?
Find a vacancy that works for you. Send us your CV to receive a personalized offer.
Find me a jobWe are looking for a Senior AI Engineer to design and deliver LLM-powered applications and agentic systems that solve real problems for our users and teams. You'll work across the full applied-AI stack — from retrieval-augmented generation (RAG) pipelines and prompt design to multi-step agents that reason, use tools, and automate end-to-end workflows.
This is a senior builder's role. You'll take challenging use cases from an ambiguous problem to production: choosing the right models, implementing agentic architectures, wiring up retrieval and tooling, defining how we evaluate quality, and shipping reliable applications at scale.
Responsibilities
- Design and deliver production LLM applications — chat, copilots, assistants, and autonomous workflows — from concept to scale
- Build and implement RAG pipelines: chunking, embeddings, vector search, reranking, and grounding to reduce hallucination and improve relevance
- Develop agentic architectures: multi-step reasoning, tool/function calling, planning, memory, and multi-agent orchestration
- Contribute to the automation of business and engineering workflows using agentic AI and workflow automation
- Define prompt and context strategies; build evaluation harnesses and uphold quality, latency, and cost standards
- Integrate LLMs with internal data, APIs, and tools via connectors, function calling, and structured outputs
- Implement guardrails, safety, and observability for AI systems (tracing, evals, monitoring for quality and drift)
- Collaborate with product, data, and platform teams to turn ambiguous problems into shipped AI features
- Share knowledge with fellow engineers and participate in design reviews
Requirements
- 3+ years in software or ML engineering, including recent, hands-on experience building and shipping applications with LLMs
- Proven experience delivering applied-AI systems end to end
- Proficiency in Python at an advanced level
- Hands-on background in building RAG systems — embeddings, retrieval, and reranking with vector databases (Pinecone, Qdrant, Milvus, or pgvector)
- Expertise in LLM APIs and orchestration frameworks (OpenAI, Anthropic, LangChain, or LlamaIndex)
- Skills in designing agentic architectures — tool use, function calling, planning loops, and agent orchestration in production
- Competency in automating workflows with agentic AI or workflow-automation tooling
- Knowledge of prompt engineering and structured/JSON output techniques
- Capability to design evaluations and reason about LLM quality, cost, and latency trade-offs at scale
- Excellent command of written and spoken English (B2+ level)
Nice to have
- Familiarity with multi-agent frameworks (LangGraph, CrewAI, or AutoGen)
- Experience with fine-tuning, adapters (LoRA), or model distillation
- Understanding of MLOps/LLMOps — deployment, versioning, and monitoring of AI systems, including model serving and inference optimization
- Knowledge of AI safety, guardrails, and evaluation frameworks (Ragas, LangSmith, or promptfoo)
- Background in cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes)