Senior AI Engineer (Full Stack)

New

NortalAI software

Location: Latin America - RemoteFull-TimeSenior

Salary not disclosed

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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: PythonETLLangChain

Have 5–7+ years of software engineering experience.
Bring a strong foundation in backend development, distributed systems, asynchronous programming, API design, microservices, and database management.
Have 2+ years of hands-on experience designing, developing, and deploying production AI applications using LLM APIs, RAG pipelines, and agentic workflows.
Have 3+ years of data engineering and ETL experience building pipelines and integrating enterprise systems such as CRM, ERP, CPQ, or marketing platforms.
Have knowledge of data transformation, entity resolution, schema design, and data quality.
Have 3+ years of Python or equivalent backend development experience and the ability to maintain production-grade AI applications.
Have 1–2+ years of experience with vector databases such as Pinecone or Pgvector, or equivalent technologies.
Have 1–2+ years of experience with LangChain, LlamaIndex, or equivalent AI frameworks for RAG systems and agentic workflows.
Have 1+ year of experience with AI evaluation and safety, including hallucination detection, prompt injection mitigation, data leakage prevention, and safety guardrails.
Have 1+ year of experience with semantic caching, model routing, inference cost optimization, or latency reduction.
Have 2+ years of experience deploying and maintaining applications on AWS, Azure, or GCP.
Have 2+ years of experience delivering technical solutions with business and engineering stakeholders and supporting production adoption.

Transform ambiguous business challenges into functional, scalable AI features, applications, and standalone tools.
Design and implement AI memory architectures and vector stores that preserve context and comply with data retention, privacy, and deletion policies.
Build data pipelines and entity-resolution logic to integrate enterprise systems and create unified data sources for AI agents.
Collaborate with business units to understand workflows, identify AI improvement opportunities, and prioritize valuable use cases.
Partner with stakeholders throughout development and implementation to address business needs and support end-user adoption.
Gather and communicate deployment feedback to engineering and platform teams about model performance, limitations, and improvement opportunities.
Build continuous evaluation pipelines for hallucinations, prompt injections, data leakage, and performance regressions.
Implement semantic caching, intelligent model routing, and inference optimization to reduce response times and operating costs.

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