Ai engineer LLM

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, Lang Chain, or Llama Index;
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 Lang Graph, CrewAI, or Auto Gen; 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, Lang Smith, 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.