AI Engineer

• Configure and customize agentic tools — including GitHub Copilot (agent and plan modes) — to fit the squad's specific context, codebase, and delivery standards.
• Design and maintain integration roadmaps that define how AI capabilities plug into each stage of the SDLC, from requirements and technical solution through development, testing, code review, and deployment — without disrupting the client's existing, mature DevOps pipeline.
• Ensure output quality upstream and downstream: review, validate, and refine what AI agents produce at each stage, identifying deviations early and adjusting configurations, prompts, and guardrails to keep quality consistent and trustworthy.
• Build and refine prompt engineering assets — including specification prompts, code generation instructions, test generation templates, and documentation workflows — tailored to the squad's architecture and ways of working.
• Conduct tooling audits and support value stream mapping sessions, identifying bottlenecks in the SDLC where the right agentic capability can eliminate waiting time and reduce lead time.
• Define and implement guardrails and ethics and compliance standards for AI usage within the development workflow, ensuring responsible and governed adoption aligned with the client's policies.
• Pair daily with developers, QAs, and BAs to embed agentic practices into real work items, coaching team members on effective agent use and gradually transferring ownership as the squad gains autonomy.
• Develop and maintain technical documentation that captures agent configurations, integration patterns, and best practices, feeding both the squad's day-to-day operations and the final Agentic SDLC Playbook.
• Continuously monitor AI system performance — adoption rates, output quality, cycle time impact — and iterate on configurations to drive measurable improvements every two-week cycle.
• Support problem-solving at the intersection of AI tooling and software delivery, acting as the technical reference when agents produce unexpected results or when new SDLC stages need to be instrumented.

Senior AI Engineer

• Design, develop, test, and maintain production software services and APIs
• Integrate AI/ML capabilities into enterprise software, including LLM-based features and RAG (Retrieval-Augmented Generation) pipelines
• Build agentic AI systems at a high level, designing tool-calling workflows, orchestrating multi-step autonomous tasks, and connecting models to internal services and data sources
• Work with model APIs, prompt engineering, and evaluation of AI outputs
• Contribute to code through pair programming and code reviews
• Write automated tests (unit, integration) and technical documentation
• Collaborate using version control (Git) and CI/CD pipelines
• Participate in team discussions, architecture decisions, and contribute ideas

AI Services Developer

• Work on the platform with a strong focus on MLOps/LLMOps, backend, and integrations.
• Build and maintain automation pipelines for continuous deployment of ML and LLM models.
• Monitor model performance, detect drift, and automate reprocessing/retraining.
• Develop and integrate APIs for communication between AI models and internal systems using FastAPI.
• Work with advanced orchestration in Kubernetes and Airflow to ensure scalability and resilience.

• Provide technical leadership for the full lifecycle of generative AI full-stack solutions (front-end React/Node, back-end Python), applying Spec-Driven Development (specification and tests before implementation) and Harness Engineering (automated validation of the rendered artifact) as the single engineering standard — complementary, not competing approaches;
• Architect and operate RAG, LLMOps, and MLOps pipelines on Azure AI Foundry, ensuring network isolation, sandboxing for execution of code generated by LLMs, and end-to-end traceability;
• Implement governance, security, and privacy controls for AI by design — model risk management, prevention of prompt injection/data leakage, and minimization and anonymization of personal and health data in accordance with LGPD;
• Establish and maintain automated quality gates (QA harness) for AI-generated outputs, providing deterministic feedback (coordinates/metrics) instead of subjective evaluation;
• Serve as the technical reference for internal teams and vendors, defining architecture standards, code review practices, and acceptance criteria for AI solutions;

Artificial Intelligence Engineer, AI

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AI Engineer II

• Desenvolver aplicações que integram LLMs (Gemini, Claude) via Vertex AI: pipelines de RAG, function calling, orquestração de agentes e automações inteligentes.
• Ser dono(a) da orquestração de IA e da camada de aplicação — o engenheiro de dados cuida da ingestão e do vector store, você consome e orquestra.
• Subir e sustentar a solução em produção no GCP: infraestrutura como código, CI/CD, IAM e observabilidade.
• Acompanhar custo e latência dos modelos em produção, mantendo a solução eficiente.
• Implementar guardrails e avaliação de qualidade (evals) em parceria com o PM.
• Construir integrações com sistemas financeiros, RPAs e ferramentas de workflow.
• Escrever código de produção com testes, segurança e boas práticas.

Engenheiro de Software Sênior – IA Aplicada

• Develop AI agents.
• Build automation flows.
• Integrate agents with SAP S/4 HANA, Microsoft 365 and other systems.
• Develop APIs.
• Implement RAG (Retrieval-Augmented Generation).
• Create knowledge bases.
• Integrate vector databases.
• Develop tools for agents.
• Deploy agents to production.
• Monitor performance.
• Continuously evolve the agents.

AI Agent Developer

• Lead a multidisciplinary squad, fostering a collaborative, autonomous, and results-oriented environment.
• Orchestrate the use of AI agents across the entire Software Development Life Cycle (SDLC), ensuring their application in appropriate contexts and adherence to quality and governance standards.
• Facilitate Agile ceremonies (Planning, Daily, Review, and Retrospective), adapting the workflow to a development model supported by AI.
• Manage the backlog in partnership with Product Managers and stakeholders, prioritizing deliveries that provide the greatest business value.
• Continuously monitor the quality of human deliverables and outputs generated by AI agents, refining workflows, prompts, and configurations as needed.
• Define and track productivity, quality, efficiency, and AI adoption metrics for the squad.
• Remove impediments and manage risks, ensuring predictability in deliveries.
• Promote continuous alignment between product, business, and technology, ensuring transparent communication with all stakeholders.
• Encourage technical development of the team and continuous improvement of engineering practices and collaboration with AI.
• Identify opportunities for continuous improvement, automation, and optimization of the development process.

AI Squad Lead – AI Orchestration, Software Delivery

• Lead, guide, and mentor the team, fostering professional growth and technical autonomy.
• Conduct and actively participate in code reviews, ensuring quality, scalability, and maintainability of solutions.
• Define and promote development and architecture best practices.
• Serve as a technical escalation point for the team, assisting in resolving complex issues and bottlenecks.
• Collaborate with management to set priorities, plan the technical roadmap, and manage the team backlog.
• Design and architect robust, scalable AI systems for production deployment.
• Ensure the performance and stability of AI agents in production through continuous monitoring and optimization.
• Explore and test new technologies, frameworks, and AI models to keep the company at the forefront of innovation.
• Document architectures, processes, and technical decisions clearly and accessibly.
• Work closely with Product, Engineering, and business teams to understand challenges and translate them into viable AI solutions.
• Effectively communicate progress, challenges, and outcomes of AI projects to technical and non-technical stakeholders.
• Contribute to the long-term vision for AI, identifying opportunities to leverage artificial intelligence to drive business growth.

Tech Lead, AI

• Conduct organizational diagnostics, redesign workflows and operating models, support technology adoption, and use AI tools to demonstrate value inside real client processes
• Map current-state workflows end-to-end and diagnose root causes of operational friction
• Redesign processes for target-state operations
• Establish performance baselines before engagements begin; build the measurement framework that tracks improvement over the engagement
• Identify value gaps in stalled implementations
• Build reporting cadences that give clients a clear line from initiative to impact
• Build working AI-powered prototypes directly
• Use prototypes as the primary tool for stakeholder alignment
• Evaluate where AI changes a workflow and where it is a distraction
• Support rollout of new tools and workflows; ensure adoption is grounded in process design and change management
• Design enablement materials that make new technology stick;
• Support complex engagement scoping where client needs are ambiguous or span multiple services
• Conduct diagnostic assessments

Forward Deployed Engineer, AI and Business Transformation

• Develop and deploy Generative Artificial Intelligence systems and multi-agent AI agents (stateful and autonomous).
• Design and optimize RAG (Retrieval-Augmented Generation) architectures using vector databases to build intelligent knowledge bases.
• Develop and test advanced prompt engineering strategies, few-shot prompting techniques, and fine-tuning approaches for foundation models.
• Collaborate on building robust connectors and APIs to integrate AI models with enterprise systems such as CRMs, ERPs, and e-commerce platforms.
• Ensure governance, security, reduction of hallucinations, and cost optimization for LLM API calls.

• Design and execute advanced AI/ML models – including fine-tuned LLMs and RAG architectures – for integration into both internal tooling and external client-facing products.
• Lead experimentation efforts: clearly define hypotheses, design interpretable tests, analyze results rigorously, and communicate findings effectively.
• Serve as the technical SME for GenAI/LLM-powered tools, from foundational architecture to model optimization and deployment.
• Work with software and data engineering to productionize models and contribute to robust CI/CD pipelines with MLOps/LLMOps best practices.
• Stay current on emerging methods in AI – e.g., agentic workflows, memory-augmented systems, multimodal embeddings – and bring those ideas to life in real-world applications.
• Promote AI adoption internally by educating and empowering cross-functional partners while improving processes and workflows.
• Manage your time and projects effectively, ensuring clear progress updates and proactive communication with collaborators.
• Write production-grade Python code to support models, pipelines, and experimental frameworks.
• Work with the highest degree of ethics and empathy, recognizing that AI can be a potentially disruptive force for organizations and individuals.

Senior AI/ML Engineer – Technology

• We are looking for AI-first engineers who use Generative AI as a foundation of software engineering.
• This role applies AI across the Product Development Lifecycle, from requirements and design to deployment and operations.
• You will design and build AI Agents, apply prompting techniques, and implement architectures like RAG.
• You will collaborate closely with product and engineering teams to embed AI into daily workflows.
• Translate product and engineering challenges into AI solutions.
• Build and deploy AI Agents with reasoning and memory integration.
• Apply prompt engineering and RAG to real-world use cases.
• Collaborate with teams to embed AI into requirements, coding, testing, and operations.
• Prototype and test AI solutions, balancing experimentation with production readiness.
• Support demonstrations of AI use cases for clients and internal stakeholders.
• Stay current with advances in Generative AI and bring them into practice.
• Communicate findings and trade-offs clearly in English to both technical and business audiences.

• Design and evolve conversational agents in production at high volume, with WhatsApp as the primary channel
• Orchestrate multi-agent systems: intent routing, handoff, session management, memory and failure recovery
• Develop tools and MCP servers that connect agents to the business domain
• Instrument LLM observability (Langfuse / LangSmith / OpenTelemetry): cost, latency, tool-call rate and production quality
• Build and maintain automated evals — no prompt or model change goes live without measurement
• Integrate multiple LLM providers (OpenAI, Anthropic, Groq) with fallback strategies and cost controls
• Implement RAG pipelines (embeddings, pgvector, reindexing)
• Create and maintain Claude Code skills, subagents and workflows that automate the delivery cycle
• Ensure reliability: rate limiting, durable queues, anti-bot guardrails, error tracking
• Operate agents on Kubernetes: GitOps deployments, resources, metrics and manifests
• Collaborate with product and design during discovery

Senior AI Software Engineer

• Foco em Modernização, eficiência e crescimento através de P&D em IA
• Pesquisa em IA;
• Modernização de software legados;
• Desenvolvimento de novos Produtos;

Engenheiro de Software Senior – P&D IA

• You will be responsible for designing, implementing, and evolving agentic flows that transform the SDLC of development teams at a strategic benefits-sector client.
• While the AI Orchestrator coordinates the process, the AI Engineer builds the infrastructure that makes automation possible — from custom agents to integrations with the delivery pipeline.
• Customize and configure generative AI platforms to meet the client's specific SDLC needs (skills, personas, prompts, contexts)
• Develop and maintain agentic flows integrated with the development pipeline (Azure DevOps, Git, CI/CD)
• Integrate AI agents across multiple stages of the SDLC: analysis, refinement, development, testing, and deployment
• Tune agent parameters and strategies for optimal performance at each stage
• Plan and execute the AI infrastructure roadmap together with the AI Orchestrator
• Automate development steps while maintaining quality, security, and compliance standards (SonarQube, Veracode, test coverage)
• Identify and mitigate technical risks related to agent usage in production
• Ensure adherence to ethical and regulatory standards in implemented AI solutions

Senior AI Architect – Agentic SDLC

• Design realistic **software engineering challenges** involving debugging, feature development, code refactoring, and performance optimization
• Develop **reference implementations** and reproducible software environments
• Write high-quality code in **Python**, **Java**, **Go**, **Rust**, **C++**, or **TypeScript**
• Analyze, troubleshoot, and resolve complex software defects
• Refactor and optimize existing codebases for performance, maintainability, and scalability
• Document technical decisions, implementation approaches, and engineering rationale
• Review and validate technical solutions submitted by other contributors
• Apply software engineering best practices across diverse programming languages and systems

Senior Software Engineer – AI Training

• Define the technical architecture for incorporating AI (LLMs, RAG, agents) into the company’s products
• Act as the technical and strategic AI point of reference, supporting other departments in identifying application opportunities
• Evaluate and recommend AI and cloud tools, platforms, and providers suitable for each use case
• Integrate AI solutions with existing systems, developed primarily in C#/.NET
• Ensure MLOps best practices, monitoring, and governance of models in production
• Safeguard security, privacy and compliance (LGPD) in implemented solutions
• Translate business needs into viable technical solutions, prioritizing impact and scalability

AI Architect

• Architect backend systems that power AI-based products: Design and implement scalable backend services that enable AI-driven capabilities, including APIs, orchestration layers, and data pipelines that connect AI models to real-world product workflows.
• Build reliable integration layers for LLM and AI services: Develop backend systems that integrate with large language models (LLMs) and other AI services, ensuring robust request handling, prompt orchestration, observability, and the necessary safeguards for production-grade AI systems.
• Design distributed systems that scale with AI workloads: Architect microservices and event-driven systems capable of handling asynchronous workloads, model inference pipelines, and high-throughput data processing.
• Establish technical standards for backend architecture and reliability: Lead by example with high-quality, maintainable code, setting standards for service design, API contracts, testing, and observability across the engineering organization.
• Advocate AI-driven development practices: Leverage modern AI development tools to improve engineering velocity, ensuring that AI-assisted code and workflows maintain high standards of quality, security, and maintainability.
• Collaborate across engineering and product teams: Work closely with frontend engineers, AI researchers, product managers, and platform teams to design backend systems that enable integrated, AI-powered product experiences.
• Mentor engineers and raise engineering quality: Provide technical guidance through code reviews, architecture discussions, and mentorship, helping engineers grow while improving overall engineering standards.
• Treat technical debt as strategic improvement opportunities: Continuously improve system architecture, reliability, and developer experience by proactively identifying areas for refactoring, automation, and platform enhancements.

Lead Software Engineer – AI

• Drive complex AI engineering workstreams across multiple business areas, use cases, products, or stakeholder groups.
• Define AI engineering approaches that align business goals, workflow opportunities, orchestration patterns, integration requirements, and measurable implementation outcomes.
• Translate ambiguous stakeholder needs into structured implementation plans, workflow logic, prompt orchestration, retrieval patterns, tool-calling approaches, and solution recommendations.
• Guide the implementation of AI-enabled applications, agents, assistant-style experiences, and workflow components that connect models to practical business processes.
• Establish and reinforce best practices for prompt orchestration, tool use, context handling, retrieval flow, API integration, workflow composition, response processing, testing, and code quality.
• Design and improve reusable implementation patterns for use cases such as summarization, extraction, question answering, search augmentation, conversational support, workflow automation, grounded insight delivery, and agent-assisted task execution.
• Contribute to delivery planning, prioritization discussions, and quality standards across AI engineering engagements.
• Mentor, coach, and support practitioners through feedback, guidance, and performance development.
• Review deliverables for clarity, technical rigor, quality, consistency, safety, and business usefulness.
• Help teams improve workflow reliability, integration quality, testing discipline, and implementation reuse across projects.
• Collaborate with AI Scientists, Data Scientists, Data Engineers, Analytics Engineers, and Architects to align AI solutions with business needs, governed data access, platform realities, and technical constraints.
• Contribute to hiring, onboarding, capability development, and team maturity within the AI engineering practice.
• Follow established governance, privacy, safety, and responsible AI-use standards across the work of the team.

AI Engineer jobs in Brazil

See all jobs on JobTailor

Senior AI Engineer

AI Services Developer

Senior AI Engineer

Artificial Intelligence Engineer, AI

AI Engineer II

Engenheiro de Software Sênior – IA Aplicada

AI Agent Developer

AI Squad Lead – AI Orchestration, Software Delivery

Tech Lead, AI

Forward Deployed Engineer, AI and Business Transformation

AI Engineer

Senior AI/ML Engineer – Technology

AI Engineer

Senior AI Software Engineer

Engenheiro de Software Senior – P&D IA

Senior AI Architect – Agentic SDLC

Senior Software Engineer – AI Training

AI Architect

Lead Software Engineer – AI

Senior AI Engineer