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Senior AI Engineer with RAG and Agentic architectures

Remote in Türkiye
AI Solution Engineering& 6 others
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We 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)