AI Engineer

About the project
You will join a team building a modern data & knowledge platform focused on advanced analytics, semantic enrichment, and intelligent information retrieval. The environment combines technologies such as Snowflake, Apache NiFi, GraphDB, and Elasticsearch to support scalable data processing and knowledge discovery solutions. A key aspect of the role is AI-supported software development, including prompt engineering, LLM-assisted coding, and rapid prototyping with modern AI tooling. The team works on end-to-end flows covering data ingestion, transformation, semantic enrichment, indexing, and AI-assisted insights generation. This is a highly collaborative, cross-functional environment where engineering quality, automation, and innovation are equally important.\

You’re ideal for this role if you:

• Have 3+ years of professional experience in software development, backend engineering, or data platform development

• Have hands-on experience with AI-supported development, including prompt engineering and effective usage of coding copilots / LLM tools

• Can translate business and technical requirements into structured prompts and iterative AI-assisted workflows

• Have strong programming skills in Python, Java, or TypeScript (Python preferred)

• Have experience building APIs, integrations, and services working with data platforms or distributed systems

• Understand software engineering best practices including testing, CI/CD, Git, and code quality standards

• Are comfortable working in agile, cross-functional teams and communicating with both technical and non-technical stakeholders

• Have experience with AI-assisted coding, refactoring, debugging, documentation, and rapid prototyping approaches

Nice to have:

• Experience with Snowflake, including querying, transformations, and data modeling

• Knowledge of Apache NiFi and/or workflow orchestration tools such as Airflow

• Familiarity with GraphDB and semantic technologies (RDF, OWL, SPARQL, ontology modeling)

• Experience working with Elasticsearch, including indexing, analyzers, and relevance tuning

• Understanding of RAG architectures, embeddings, vector search, and hybrid retrieval approaches

• Experience with enterprise-grade security, governance, and secrets management

• Familiarity with MLOps or LLMOps concepts such as model management, prompt versioning, and evaluation pipelines

Your day-to-day responsibilities include:

• Building and maintaining AI-assisted platform components such as intelligent search, enrichment, summarization, and classification services

• Using AI-supported development practices including prompt engineering, LLM-assisted coding, and rapid prototyping to accelerate delivery

• Developing integrations and services connected with Snowflake, Apache NiFi, GraphDB, and Elasticsearch

• Collaborating with Data Engineers and Semantic Engineers on end-to-end data and knowledge processing flows

• Designing and improving semantic enrichment, indexing, and AI-assisted retrieval solutions

• Implementing automated tests, CI/CD pipelines, observability, and performance optimization mechanisms

• Evaluating and improving AI output quality through grounding strategies, hallucination mitigation, and reproducibility practices

• Preparing technical documentation, prompting guidelines, and best practices for AI-assisted engineering workflows