AI Engineer

• Build and evolve the core architecture of the AI agent platform, ensuring standardization, governance, and high reusability
• Build and integrate robust mechanisms for tracking, metrics, evaluation, logging, observability, and auditing
• Evaluate AI agent initiatives by understanding their needs, behavioral patterns, interactions, and orchestration requirements
• Contribute to bringing critical components in-house, ensuring control, security, and consistency across the ecosystem
• Collaborate with other AI initiatives, ensuring technical alignment and the sharing of lessons learned
• Develop foundational MVPs, such as standardized documentation, agent templates, monitoring tools, and observability protocols

Senior Artificial Intelligence Engineer – Agents

• Develop specialist agents coordinated by a supervisor, with agentic loops (ReAct) and auditable decision logging.
• Build MCP Servers that connect agents to operational APIs (.NET) and ML models through OpenAPI contracts.
• Implement the MCP Host using LangChain / Semantic Kernel and LLMs via Azure OpenAI.
• Implement Human-in-the-Loop (HITL) workflows for high-impact decisions.
• Ensure agentic security: per-agent identity (OAuth2), tool-level scoping, idempotency, prompt-injection sanitization, and PII leakage prevention.
• Version prompts (PromptOps) and instrument agent observability (LangSmith or similar).
• Integrate with event-driven architecture (Azure Service Bus / Kafka).

AI Engineer – Agentic Applications

• Develop and evolve Generative AI and agentic applications
• Build RAG-based solutions and enterprise knowledge retrieval components
• Implement AI agents that interact with tools, APIs, databases, and enterprise systems
• Work with orchestration patterns such as multi-step workflows, tool use, and Human-in-the-Loop
• Integrate LLM providers and cloud AI services
• Design maintainable and testable components following good software engineering practices
• Implement and maintain evaluation, observability, guardrails, and monitoring capabilities
• Write automated tests and contribute to CI/CD pipelines
• Troubleshoot and improve AI applications in production with support from senior team members
• Collaborate with engineers, architects, product teams, and business stakeholders
• Work closely with senior engineers, architects, and product teams to build and evolve AI solutions from experimentation to production

Mid-Level AI Engineer

• Own AI solutions from technical discovery through production and continuous improvement
• Design and evolve Generative AI, RAG, and agentic applications
• Define technical approaches considering quality, scalability, security, cost, maintainability, and operational impact
• Design agents and workflows that interact with tools, APIs, databases, messaging systems, and enterprise platforms
• Define orchestration patterns such as Human-in-the-Loop, long-running workflows, retries, fallback strategies, and deterministic controls
• Establish evaluation, observability, guardrails, and reliability practices for AI applications
• Guide model, retrieval, framework, and infrastructure choices based on technical trade-offs
• Troubleshoot complex production issues involving models, prompts, retrieval, tools, integrations, and infrastructure
• Collaborate with architects, engineers, product teams, and business stakeholders, providing technical guidance when needed
• Support other engineers and contribute to engineering standards and best practices for AI solutions

Senior AI Engineer

• Review and evaluate AI-generated code across programming languages and software engineering scenarios
• Assess code for correctness, reliability, security, scalability, readability, and maintainability
• Identify bugs, logical errors, incomplete implementations, edge-case failures, and architectural weaknesses
• Analyze unfamiliar codebases and understand the impact of proposed changes
• Review bug fixes, feature implementations, refactoring, API integrations, configuration changes, and database operations
• Compare alternative implementations and determine whether they satisfy technical requirements
• Rewrite or improve code to create high-quality reference solutions
• Provide clear technical explanations of identified issues and recommended improvements
• Develop evaluation criteria, technical annotations, and rubrics for code-quality assessment
• Collaborate with researchers and engineering teams to design coding benchmarks and improve LLM evaluation methodologies

Senior Software Engineer – AI Code Evaluation

• 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

• Develop prototypes and MVPs using generative AI, automation, and integrations
• Create automated workflows using n8n and API integrations
• Work with tools such as Claude, Claude Code, Lovable, OpenAI, and similar platforms
• Develop or enhance solutions using two or more of the following languages and technologies: Node.js, React, PHP, and Python
• Integrate internal systems, databases, web applications, and AI tools
• Support the technical design of agents, copilots, assistants, and intelligent workflows
• Assess the technical feasibility, effort, risks, and architecture of solutions
• Support security best practices, logging, authentication, and the secure use of data
• Document solutions and support the evolution of MVPs into production
• Provide consulting support in mapping As-Is processes and proposing To-Be processes
• Partner with product and business teams to transform efficiency opportunities into practical solutions

Full-Stack AI Engineer – Mid-Level/Senior

• Develop Python-based customer service orchestrators for voice and chat channels
• Configure security layers using NeMo Guardrails
• Integrate enterprise tools (CRM, Billing) through MCP Servers
• Ensure trace instrumentation for monitoring costs and performance via Langfuse
• Implement low-latency, high-accuracy conversational flows
• Collaborate with SREs on deployment to OKE
• Collaborate with data scientists to refine the quality of LLM responses
• Develop and orchestrate Generative AI agents on GPU infrastructure in Oracle Cloud (OCI)

Senior AI Developer

• Develop, integrate, and implement solutions on the ServiceNow platform, focusing on hyperautomation and Artificial Intelligence
• Design and implement customized solutions using Script Includes, Business Rules, Client Scripts, UI Actions, and Flow Designer
• Configure, customize, and monitor Now Assist (Generative AI) use cases across modules such as ITSM, CSM, HRSD, and Creator
• Optimize the use of ServiceNow’s native Prompt Engineering capabilities to generate incident summaries, automated responses, code, and contextual recommendations
• Create, train, and fine-tune Machine Learning models for automated ticket routing, pattern detection, and incident prevention
• Develop conversational flows in Virtual Agent, integrating NLU and LLMs for self-service and intelligent request resolution
• Implement and optimize AI Search for self-service portals (Service Portal / Now Experience)
• Use AI-assisted tools for JavaScript code generation, automated workflow creation, and testing
• Connect ServiceNow to external AI services through IntegrationHub and REST/SOAP APIs
• Ensure AI and data governance, complying with privacy and information security requirements and alignment with CSDM/CMDB architecture

AI Engineer jobs in Brazil