AI Engineer

Devire Outsourcing IT is a collaboration model dedicated to IT specialists based on self-employment (B2B), executing projects for clients running innovative and modern projects. The client operates in the banking sector.

• Knowledge of tools and frameworks for building services using LLM, e.g., langchain4j, google-adk.
• Knowledge of Google BigQuery including modeling, cost and performance optimization, permissions, views, and routines.
• Very good knowledge of Java and/or Python.
• Experience in building and maintaining data pipelines.
• Knowledge of DevSecOps patterns.

Nice to have:

• Knowledge of semantic/metrics layer solutions, caching, preaggregations, and governance metrics in BigQuery/GCP or similar.
• Experience building LLM/GenAI solutions for corporate data (chat with data, Q&A).
• Practical knowledge of NL2SQL approaches and risk mitigation (hallucinations, incorrect joins, excessive scans).
• Experience building RAG: embeddings, chunking, re-ranking, response quality assessment.
• Experience with vector databases (e.g., Pinecone, Weaviate, Milvus) and/or graph databases (e.g., Neo4j).
• Experience evaluating GenAI systems: offline/online evals, A/B testing of prompts.
• Knowledge of Rust.
• Experience working with Kubernetes.
• Experience with GCP services: Cloud Run, Pub/Sub.
• Experience working in regulated environments (banking): audit, compliance, data classification.

• Designing, developing, and maintaining AI/LLM-based solutions and 'Chat with Data' systems.
• Creating and developing applications utilizing language models integrated with corporate data.
• Designing and developing data processing pipelines in Data Lake and Data Warehouse environments.
• Implementing and optimizing solutions within the Google Cloud Platform ecosystem, focusing on BigQuery.
• Collaborating on data model design and preparing solutions to meet business needs.
• Optimizing performance of processes and SQL queries while ensuring data quality and security.
• Participating in analysis, design, testing, and deployment of new solutions.
• Troubleshooting production issues and supporting maintenance of deployed systems.
• Working according to DevSecOps approach and adhering to security and quality standards.