ai engineer for LLM applications

The company develops enterprise software products, open source solutions, and accelerators.

• Architect and deliver production LLM applications, including chat, copilots, assistants, and autonomous workflows;
• Construct and implement RAG pipelines with chunking, embeddings, vector search, reranking, and grounding;
• Create agentic architectures with multi-step reasoning, tool and function calling, planning, memory, and multi-agent orchestration;
• Support the automation of business and engineering workflows through agentic AI and workflow automation;
• Establish prompt and context strategies, construct evaluation harnesses, and maintain quality, latency, and cost standards;
• Connect LLMs with internal data, APIs, and tools through connectors, function calling, and structured outputs;
• Deploy guardrails, safety, and observability for AI systems, including tracing, evaluations, and quality and drift monitoring;
• Partner with product, data, and platform teams to transform ambiguous problems into shipped AI features;
• Exchange knowledge with fellow engineers and take part in design reviews.

• 3+ Years of software or ML engineering experience, including recent hands-on experience building and shipping LLM applications;
• Demonstrated track record of delivering applied-AI systems end to end;
• Advanced Python proficiency;
• Experience building RAG systems with embeddings, retrieval, reranking, and vector databases such as Pinecone, Qdrant, Milvus, or pgvector;
• Expertise in LLM APIs and orchestration frameworks such as OpenAI, Anthropic, LangChain, or LlamaIndex;
• Experience designing production agentic architectures with tool use, function calling, planning loops, and agent orchestration;
• Competence in automating workflows with agentic AI or workflow-automation tooling;
• Knowledge of prompt engineering and structured/JSON output techniques;
• Ability to design evaluations and reason about LLM quality, cost, and latency trade-offs at scale;
• Strong written and spoken English at B2+ level;
• Nice to have: familiarity with multi-agent frameworks such as LangGraph, CrewAI, or AutoGen; background in fine-tuning, adapters such as LoRA, or model distillation; understanding of MLOps/LLMOps, including deployment, versioning, monitoring, model serving, and inference optimization; knowledge of AI safety, guardrails, and evaluation frameworks such as Ragas, LangSmith, or promptfoo; expertise in cloud platforms such as AWS, GCP, or Azure and containerization with Docker or Kubernetes.

• Remote work is available in Georgia and Armenia.