Senior AI Engineer (Full Stack)

NortalAI software

Location: Latin AmericaFull-TimeSenior

Salary not disclosed

Apply NowOpens the employer's application page

Job Details

Experience: 5–7+ years of Software Engineering Experience; 2+ years of Production AI / LLM Experience; 3+ years of Data Engineering & ETL Experience; 3+ years of Python or Equivalent Backend Development; 1–2+ years of Vector Database & AI Memory Experience; 1–2+ years of AI Frameworks & Orchestration; 1+ year of AI Evaluation & Safety Experience; 1+ year of AI Performance Optimization; 2+ years of Cloud Experience; 2+ years of Stakeholder-Facing Technical Delivery
Required Skills: PythonETLData engineeringLangChain

Have 5–7+ years of software engineering experience, including backend development, distributed systems, asynchronous programming, API design, microservices, and database management.
Have 2+ years of production AI/LLM experience with LLM APIs, RAG pipelines, and agentic workflows.
Have 3+ years of data engineering and ETL experience integrating enterprise systems.
Have experience with data transformation, entity resolution, schema design, and data quality.
Have 3+ years of Python or equivalent backend development experience.
Have 1–2+ years of vector database and AI memory experience, such as Pinecone or Pgvector.
Have 1–2+ years of experience with LangChain, LlamaIndex, or equivalent AI orchestration frameworks.
Have 1+ year of experience with AI evaluation, hallucination detection, prompt injection mitigation, data leakage prevention, or safety guardrails.
Have 1+ year of AI performance optimization experience, including semantic caching, model routing, cost optimization, or latency reduction.
Have 2+ years of experience deploying and maintaining applications on AWS, Azure, or GCP.
Have 2+ years of stakeholder-facing technical delivery experience supporting production adoption.

Transform business challenges into scalable AI features, applications, and standalone tools.
Design AI memory architectures and vector stores that preserve context and meet data retention, privacy, and deletion policies.
Build data pipelines and entity-resolution logic across CRM, ERP, CPQ, and marketing platforms.
Collaborate with business units to identify workflows and prioritize valuable AI use cases.
Partner with stakeholders throughout development and implementation to support end-user adoption.
Gather deployment feedback for engineering and platform teams on model performance, limitations, and opportunities.
Build continuous evaluation pipelines for hallucinations, prompt injections, data leakage, and performance regressions.
Implement semantic caching, model routing, and inference optimization to reduce latency and costs.

View Full Description & ApplyYou'll be redirected to the employer's site