AI Engineer

• Develop user-facing interfaces in TypeScript and React, with backend services and APIs in Python or Node
• Implement agentic behavior including orchestration, tool and function calling, memory, and guardrails
• Build retrieval-augmented generation pipelines covering ingestion, chunking, embeddings, and vector and hybrid search
• Design and build agents with Gemini models, Vertex AI, the Agent Development Kit, and Agent Engine
• Implement and configure Gemini Enterprise, including Agent Designer, Inbox, and agent sandboxes
• Connect Gemini Enterprise to client application landscapes through first-party and partner connectors with permissions, governance, and auditability
• Build grounded, retrieval-backed applications with Vertex AI Search, RAG Engine, Google Search grounding, and BigQuery
• Implement agent interoperability through A2A and MCP
• Write evaluation suites and regression tests for LLM-powered features; monitor cost, latency, and quality in production
• Deploy on GCP, Azure, or AWS and maintain data pipelines feeding AI systems
• Use Claude Code, Gemini CLI, Codex, or Cursor daily with verification and review
• Communicate progress, trade-offs, and blockers to clients and project leads
• Support pre-sales when needed
• Mentor junior engineers
• Contribute to internal accelerators, reusable components, and engineering standards

Senior Deployed AI Engineer, Gemini

• Design module content for the FinTech Engineering Specialization at Jala University
• Develop labs
• Create a reference implementation
• Build an evaluation harness
• Prepare an instructor guide

Academic Content Developer – AI, Fraud Detection, Financial Systems

• Build and maintain Unity (C#) field applications for sketching, configuring, and publishing pipe, duct, and other MEP work from jobsites
• Write production Unity code covering scene and prefab architecture, component lifecycle, input handling, UI, performance, and editor/runtime separation
• Own client-to-cloud integration, login, publishing, data synchronization, fidelity, and error handling between the Unity client and Stratus services
• Build and maintain automated Unity tests using NUnit, PlayMode integration tests, and EditMode unit tests
• Ship customer-facing enhancements across backend services, APIs, data, and web front end
• Design and build services and APIs connecting the web platform and cloud data
• Investigate and fix customer-reported bugs across client, service, and data layers
• Implement unit, integration, and end-to-end automated testing
• Contribute to monolith decomposition and modernization
• Use AI-assisted development tools and validate AI-generated changes with tests and engineering judgment
• Collaborate with product managers, designers, and customer-facing teams to define, design, scope, and ship features
• Coordinate deployments and incident response
• Advocate for incremental delivery
• Share knowledge through code reviews and pragmatic best practices

Unity Developer – AI Forward

• Build AI solutions for company research, document analysis, meeting summaries, and reporting
• Integrate AI with internal systems, CRM platforms, and company data
• Automate recurring tasks and measure improvements in efficiency and accuracy
• Validate AI outputs, implement human review where needed, and protect confidential information
• Maintain solutions and document processes
• Train team members
• Walk shortlisted interviewers through personally built work

Applied AI Engineer

• Design, build, and deploy production-grade data pipelines, analytics engines, and Generative AI/LLM applications using Databricks, Spark, Python/PySpark, and modern cloud ecosystems
• Serve as the primary technical leader on strategic enterprise client engagements
• Partner with C-level executives, VP/Directors of Data, and engineering teams to translate business challenges into technical solutions
• Lead complex legacy platform migrations onto the Databricks Lakehouse architecture
• Guide enterprise customers in adopting GenAI solutions, RAG architectures, model fine-tuning, and MLflow/Databricks Mosaic AI integrations
• Identify high-value technical use cases across client business units, expand Databricks platform usage, and drive measurable client ROI
• Mentor junior Solution Engineers, Architects, and Consultants
• Contribute to internal RevStar code libraries, reusable accelerators, and technical delivery best practices

Forward Deployed Engineer, AI/ML

• Create realistic, challenging coding tasks involving feature implementation and software bug fixes.
• Design tasks that reflect real-world backend engineering scenarios.
• Develop deterministic verifiers that accept reasonable valid solutions and reject incorrect outputs.
• Work with backend technologies such as Java, Node.js, Go, Rust, TypeScript, Python, C++, or similar languages.
• Evaluate code for correctness, reliability, maintainability, and performance.
• Incorporate automated testing and clear validation criteria into coding tasks.
• Identify and account for edge cases, performance bottlenecks, and potential implementation errors.
• Apply secure coding principles and cybersecurity considerations where relevant.
• Refine tasks and verification logic based on project requirements and quality standards.

Backend Software Engineer – AI Training Projects

• Lead project delivery end to end with governance, stakeholder communication, and accountability for outcomes
• Build and mentor a high-performing AI engineering team
• Establish technical standards and a culture of quality and pragmatism
• Own proposals and new business initiatives
• Define technical feasibility and communicate risks and tradeoffs to clients
• Define appropriate AI system scope and set realistic expectations
• Conduct technical reviews and architectural assessments
• Guide design and delivery of production-ready RAG systems, agentic frameworks, and LLM-powered solutions
• Lead advanced prompt engineering, including instruction design, few-shot sets, structured outputs, and tool/agent prompts
• Run feasibility assessments across prompting, RAG, fine-tuning, and classical ML
• Mentor engineers on AI system design and production deployment
• Design evaluation frameworks, including LLM-as-a-judge, recall@k, precision@k, and go/no-go gates
• Lead structured experiments across prompts, retrievers, chunking strategies, and models
• Establish practices for identifying and categorising model failures
• Set AI production reliability standards
• Build scalable inference infrastructure and CI/CD pipelines
• Automate the MLOps/LLMOps lifecycle, including tracking, versioning, deployment, monitoring, and retraining
• Design APIs, microservices, and orchestration layers for latency, cost, and reliability
• Lead infrastructure decisions balancing technical excellence and business efficiency

Senior AI Engineer

• Build AI solutions for company research, document analysis, meeting summaries, and reporting
• Integrate AI with internal systems, CRM platforms, and company data
• Automate recurring tasks and measure improvements in efficiency and accuracy
• Validate AI outputs and implement human review where needed
• Protect confidential information
• Maintain AI solutions and document processes
• Train team members
• Walk shortlisted interviewers through personally built work

• Own and develop the existing internal CRM across the Node.js/Express backend and React frontend
• Audit the codebase for gaps, bugs, technical debt, security risks, and incomplete functionality
• Create a plan to complete and stabilize the CRM
• Develop, test, and maintain lead management, communications, agent workflows, reporting, and administrative features
• Debug and resolve issues affecting live applications
• Maintain and improve AI functionality using OpenAI and Anthropic APIs
• Work within the agentic tool gateway, including tool permissions, policy controls, and safety restrictions
• Maintain real-time functionality using Redis, Socket.IO, and Twilio Voice
• Protect customer and agent data through authentication, authorization, validation, logging, and tenant isolation
• Manage deployments and infrastructure across Render, Cloudflare, AWS S3, MongoDB Atlas, GitHub, and GitHub Actions
• Maintain CI/CD and production deployment processes
• Work with the founder and operations manager to translate business requirements into technical solutions
• Build licensing and onboarding systems to replace the current GoHighLevel process
• Support additional internal systems, operational technology, and AI implementation projects
• Become the primary technical owner of the CRM and establish a stable release process and development roadmap

Senior Full Stack AI Engineer

• Define enterprise AI architecture standards, frameworks, best practices, technology roadmaps, governance, and operating models
• Evaluate emerging AI technologies and design scalable AI platforms
• Design end-to-end solutions for Generative AI, LLMs, AI Agents, Machine Learning, Predictive Analytics, and Intelligent Automation
• Translate business requirements into technical AI solution architectures and architecture blueprints
• Architect AI solutions across Microsoft Azure and AWS
• Define infrastructure requirements for model hosting, vector databases, and data pipelines
• Ensure high availability, scalability, reliability, and performance
• Design enterprise GenAI solutions using Azure OpenAI, AWS Bedrock, Anthropic Claude, OpenAI, and Gemini
• Define RAG architectures and architect multi-agent systems and AI orchestration frameworks
• Establish prompt engineering, evaluation, model governance, security, compliance, and responsible AI standards
• Collaborate with data architects and engineers on AI-ready data ecosystems, integrations, pipelines, and feature engineering
• Lead AI risk assessments and model validation initiatives
• Provide architectural leadership throughout project lifecycles and conduct architecture reviews and design approvals
• Mentor AI engineers, data scientists, and solution architects
• Collaborate with vendors and technology partners
• Advise executive leadership, present AI strategies and business cases, and communicate technical concepts to non-technical audiences
• Drive enterprise AI adoption and change management initiatives

AI Engineer jobs in Argentina

Senior Deployed AI Engineer, Gemini

Academic Content Developer – AI, Fraud Detection, Financial Systems

Unity Developer – AI Forward

Applied AI Engineer

Forward Deployed Engineer, AI/ML

Backend Software Engineer – AI Training Projects

Senior AI Engineer

Applied AI Engineer

Senior Full Stack AI Engineer

AI Architect