Hybrid AI/ML Developer – Junior, Mid

at CIVITTA

• Job title: AI/ML Developer – Junior, Mid
• Job type: Full Time
• Experience level: Junior
• Salary: €18 per hour
• Degree requirement: No Education Requirement
• Tech skills: AWS, Azure, Cloud, Django, Docker, Google Cloud Platform, Linux, Python
• Location requirements: Hybrid, Lithuania

About the role

• AI/ML Developer creating end-to-end AI features and working on complex projects in a vibrant consultancy setting. Collaborate across teams to enhance technology-driven solutions in the digital space.

Responsibilities

• Implement end-to-end AI features depending on the project, including: Computer Vision(detection/recognition pipelines, image preprocessing, evaluation, inference integration), NLP/Transformers (text classification, extraction, summarization, embeddings-based workflows), RAG / semantic search (ingestion -> chunking -> embeddings -> retrieval -> answer generation);
• Improve retrieval quality using two-stage search (vector retrieval + reranking using cross-encoders/rerankers) when relevant to the project;
• Work with vector search / vector databases in local and cloud setups: Self-hosted (Docker) and client environments, as well as cloud-managed search stacks (e.g., OpenSearch vector search, Azure AI Search hybrid/vector search);
• Build and maintain Python backend APIs (FastAPI/Django) that serve AI pipelines reliably (auth integration, error handling, basic performance considerations);
• Package solutions with Docker, help run deployments in cloud environments, and support debugging in dev/stage/prod;
• Collaborate with software engineers, data engineers, and consultants to deliver working solutions under real project timelines.

Requirements

• 2+ years of Python development (or equivalent strong practical experience) and ability to ship working backend services;
• Hands-on experience building at least two of: Computer Vision pipeline or NLP/Transformer-based pipeline, or RAG / semantic search pipeline;
• Familiarity with embeddings + vector search concepts (indexes, filtering/metadata, similarity search);
• Comfort with Docker, Git, and Linux basics (running services locally, troubleshooting dependencies);
• Practical mindset: you can translate requirements into an implementable pipeline and iterate quickly;
• Upper-intermediate English;
• Experience with IaaS cloud solutions (e.g., AWS, Azure, GCP), cross-encoders as rerankers, OpenSearch vector search, Azure AI Search hybrid retrieval and semantic reranking concepts, or basic ML serving/performance awareness would be a plus.