Senior AI Engineer – Full Stack

Nortal

. Transform ambiguous business challenges into functional, scalable, high-performing AI features, applications, and standalone tools .

Posted 11/1/2026full-timeRemoteSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing AI memory architectures and data pipelines, ensuring compliance with data policies while optimizing performance and operational costs. Proven ability to collaborate with stakeholders and transform business challenges into scalable AI solutions.

Highest-signal resume keywords

AI Memory Architecture DesignData Pipeline DevelopmentPython Backend DevelopmentAI/LLM Production ExperienceCloud Experience with AWS, Azure, or GCP

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Applicant Tracking System Keywords

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Hard Skills

Backend DevelopmentDistributed SystemsAsynchronous ProgrammingAPI DesignMicroservicesDatabase ManagementData TransformationEntity ResolutionSchema DesignData Quality

Soft Skills

CollaborationStakeholder EngagementCommunication

Tools & Technologies

PineconePgvectorSalesforceSAPBigQueryLangChainLlamaIndexETL ToolsSemantic CachingInference Optimization

Industry Keywords

AI FeaturesAI ApplicationsData Retention PoliciesPrivacy ComplianceModel Performance Evaluation

Tech Stack

Tools & technologies

AWSAzureBigQueryCloudDistributed SystemsERPETLGoogle Cloud PlatformMicroservicesPython

About the role

Key responsibilities & impact

Transform ambiguous business challenges into functional, scalable, high-performing AI features, applications, and standalone tools
Design and implement AI memory architectures and vector stores such as Pinecone and Pgvector
Ensure AI memory solutions comply with data retention, privacy, and deletion policies
Develop data pipelines and entity-resolution logic integrating CRM, ERP, CPQ, and marketing platforms such as Salesforce, SAP, and BigQuery
Collaborate with business units to identify high-value AI use cases and prioritize measurable opportunities
Partner with stakeholders through development and implementation to drive successful end-user adoption
Gather and communicate insights from real-world deployments about model performance, limitations, and improvement opportunities
Build continuous evaluation pipelines for hallucinations, prompt injections, data leakage, and performance regressions
Implement safety guardrails before production deployment
Optimize response times and operational costs through semantic caching, intelligent model routing, and inference optimization
Transform early-stage ideas into production-grade applications for teams across a global organization
Architect and develop intelligence and integration layers connecting autonomous AI agents with enterprise systems and infrastructure

Requirements

What you’ll need

5–7+ years of software engineering experience
Backend development, distributed systems, asynchronous programming, API design, microservices, and database management
2+ years of production AI/LLM experience
Experience designing, developing, and deploying AI-powered applications using LLM APIs, RAG pipelines, and agentic workflows in production
3+ years of data engineering and ETL experience
Experience building data pipelines and integrating CRM, ERP, CPQ, or marketing platforms
Knowledge of data transformation, entity resolution, schema design, and data quality
3+ years of Python or equivalent backend development
1–2+ years of vector database and AI memory experience, including Pinecone, Pgvector, or equivalent
1–2+ years of AI frameworks and orchestration experience, including LangChain, LlamaIndex, or equivalent
1+ year of AI evaluation and safety experience
Knowledge of hallucination detection, prompt injection mitigation, data leakage prevention, and model safety guardrails
1+ year of AI performance optimization experience
Experience with semantic caching, intelligent model routing, inference cost optimization, and latency reduction
2+ years of cloud experience with AWS, Azure, or GCP
2+ years of stakeholder-facing technical delivery experience
Experience collaborating with business and engineering teams to identify requirements, develop solutions, and support production adoption

Benefits

Comp & perks

Competitive USD salary
100% remote work; work from anywhere in LATAM, with access to coworking spaces across LATAM
Paid time off and national holidays according to the employee’s country regulations, with full salary
Sick leave
Annual credit for work-life balance perks
Team-building activities, coffee breaks, tech talks, and after-work gatherings
Birthday off
Wellness check-ins and professional growth support from the People Care team
Flexible work model focused on outcomes and personal well-being