Generative AI Engineer

KEY RESPONSABILITIES

• Design and implement GenAI applications using LLMs, embeddings, and prompt orchestration patterns.
• Build and optimize retrieval-augmented generation (RAG) pipelines and supporting components.
• Develop and maintain integrations with vector databases, APIs, and enterprise data sources.
• Engineer prompts, evaluation strategies, and guardrails for quality, relevance, and safety.
• Collaborate with product, architecture, and security teams to design scalable GenAI solutions.
• Monitor GenAI application performance, cost, latency, and output quality.
• Implement version control, testing, and deployment workflows for GenAI assets.
• Support troubleshooting, iterative tuning, and operational improvement of GenAI applications.
• Document solution design, limitations, risks, and technical decisions.

REQUIREMENTS

• Strong Python skills and hands-on experience building AI applications.
• Practical experience with LLM ecosystems and orchestration frameworks.
• Solid understanding of prompt engineering, RAG, embeddings, and vector search concepts.
• Experience integrating AI services through APIs and enterprise systems.
• Familiarity with cloud environments and containerization basics.
• Hands-on with Git and basic CI/CD practices.
• Awareness of AI risks such as hallucinations, privacy, prompt injection, and governance needs.
• Strong communication, experimentation mindset, and pragmatic problem-solving.

NICE-TO-HAVE

• Familiarity with vector databases and semantic search solutions.
• Exposure to fine-tuning, evaluation frameworks, or agentic workflows.
• Knowledge of responsible AI, security controls, and enterprise AI governance.
• Experience balancing model quality, latency, and cost in production use cases.