Senior GenAI Engineer

About the role

Ameriabank is looking for a Senior GenAI Engineer who designs, builds and deploys AI-powered capabilities that act as shared, reusable infrastructure for the bank. The role covers AI agent workflows, retrieval-augmented pipelines, enterprise knowledge systems and integrations with the bank's internal technology ecosystem, at the intersection of applied AI engineering, enterprise systems integration and responsible AI delivery.

Key responsibilities

• Design, develop and deploy AI automations, agent workflows and RAG pipelines across business domains, and evolve the Unified Enterprise Knowledge Base (document ingestion, extraction, entity resolution, ontology design, vector indexing, GraphRAG retrieval).
• Build reusable AI components, agent templates and reference architectures, and implement agentic AI frameworks for autonomous multi-step workflows.
• Integrate AI with enterprise systems (Jira, Confluence, TestRail, Azure DevOps), build internal APIs and microservices, and connect AI to the n8n workflow automation platform.
• Contribute to the SDLC AI Agent System covering requirements analysis, test generation, code review and documentation.
• Lead proof-of-concepts for multimodal, voice, vision and agentic AI models, evaluate them on-premise and in the cloud, and assess open-source models and tooling.
• Support Armenian language model capability, including multilingual models, fine-tuning and OCR for Armenian documents.
• Ensure AI solutions follow the bank's data classification framework, work with Information Security, Cybersecurity and Operational Risk on secure data handling and model transparency, and document architectures, pipelines and experiments.
• Maintain the deployed AI capability registry with documented ownership, performance baselines and governance records.

Required profile

• 5+ years in software engineering or applied AI/ML, with at least 2 years focused on GenAI systems in production.
• Proven experience designing and maintaining RAG pipelines and vector databases.
• Experience with graph data modeling, entity/relationship extraction, entity resolution and graph traversal/query languages such as Cypher; GraphRAG is highly desirable.
• Experience with tool-using LLM applications and multi-step AI workflows, including orchestration, state management, structured outputs, failure handling and human-in-the-loop controls; LangGraph, LangChain or LlamaIndex is desirable.
• Preferred: experience deploying AI in regulated sectors (banking, finance, healthcare), data engineering (ETL, SQL, Pandas), Agile delivery, open-source contributions and banking domain knowledge.

Required skills

• Strong Python, including testing, packaging, async programming and API development.
• API development, microservices and integration with enterprise platforms (Jira, Confluence, etc.).
• Containerization (Docker), Linux and GPU-based local deployments; on-premise AI experience preferred.
• Workflow automation with n8n or similar.