Role Description Buscamos um Machine Learning Engineer com perfil hands-on e visão de produto para atuar no ciclo completo de soluções de IA, com profundidade em: - Agentes inteligentes - Frameworks agênticos - Fine-tuning de LLMs - Treinamento e otimização de modelos clássicos de clusterização e aprendizado supervisionado - Deploy em ambientes produtivos com práticas modernas de MLOps e IA Generativa O profissional será responsável por: - Desenhar, treinar, customizar (fine-tuning) e operacionalizar modelos, agentes e pipelines multi-agente que impulsionem produtos e plataformas, com foco em escalabilidade, automação e integração em nuvem. - Assumir ownership das frentes sob sua responsabilidade. Responsibilities - Projetar e desenvolver agentes inteligentes e sistemas multi-agente ponta a ponta, incluindo orquestração, memória, ferramentas (tool use), guardrails e protocolos de comunicação (MCP, A2A). - Fine-tuning de Modelos (RL, LoRA/QLoRA) e técnicas de adaptação como distillation e prompt optimization. - Implementar e evoluir pipelines de RAG, Graph, RAG e recuperação semântica com embeddings vetoriais e bancos vetoriais (pgvector, Qdrant, Weaviate, etc.). - Projetar, treinar e validar modelos de Machine Learning e Clusterização (ex: K-Means, K-Medoids, DBSCAN, GMM, embeddings vetoriais). - Realizar deploy e monitoramento de modelos e agentes em ambiente produtivo (Cloud Run, Vertex AI, API endpoints). - Implementar pipelines de MLOps e LLMOps, com versionamento, CI/CD, testes automatizados, rastreabilidade e avaliação contínua (MLFlow, Kubeflow, Vertex Pipelines, LangSmith, Langfuse). - Desenvolver e integrar agentes e fluxos de automação utilizando ADK (Agent Developer Kit), LangGraph, CrewAI, AutoGen, N8N ou frameworks correlatos de orquestração agentic. - Estruturar frameworks de avaliação para agentes e LLMs (LLM-as-Judge, eval sets, benchmarks customizados, métricas de qualidade e alinhamento). - Identificar problemas e propor approaches técnicos antes de serem demandados, defendendo decisões de arquitetura com base em evidência técnica e validando hipóteses rapidamente. - Atuar como referência técnica de IA para o time e stakeholders, conduzindo discussões de arquitetura, code reviews críticos e mentorias para pares de engenharia e produto. - Conduzir frentes em paralelo e destravar dependências ativamente, comunicando blockers cedo e propondo caminhos alternativos quando uma frente trava. - Criar e manter documentação técnica e artefatos de engenharia, garantindo boas práticas de governança e reprodutibilidade. - Apoiar times de produto e engenharia na integração de modelos e agentes em aplicações (APIs, microsserviços, containers). - Avaliar continuamente novas abordagens de IA Generativa, arquiteturas agênticas e integração de LLMs. Qualifications - Formação em Ciência da Computação, Engenharia, Estatística, Matemática Aplicada ou áreas correlatas. - Sólido conhecimento em Python e bibliotecas de ML/AI (Scikit-Learn, PyTorch, TensorFlow, Hugging Face Transformers). - Experiência prática construindo agentes em produção com ao menos um framework agentic (ADK, LangGraph, CrewAI, AutoGen, LlamaIndex Agents ou similares). - Experiência com fine-tuning de LLM incluindo preparação de datasets, treinamento e avaliação. - Domínio de conceitos de vetorização, embeddings e busca semântica (Sentence Transformers, BERT, modelos de embedding modernos) — incluindo opinião técnica formada sobre escolha de banco vetorial e estratégias de chunking/retrieval. - Conhecimento prático de deploys em nuvem (Cloud Run, Vertex AI, SageMaker, etc.). - Vivência com ferramentas de versionamento e rastreabilidade de modelos e prompts (MLFlow, Kubeflow, Vertex Pipelines, Phoenix, LangSmith, Langfuse). - Experiência em integração via APIs e construção de microsserviços. - Entendimento de boas práticas de MLOps e LLMOps: monitoramento, logging, rollback, retraining, eval automatizado, observabilidade de agentes. - Familiaridade com MCP (Model Context Protocol) e padrões modernos de integração entre LLMs e ferramentas externas. - Ownership ponta-a-ponta de iniciativas de IA — capacidade de conduzir uma frente do problema à entrega sem necessidade de microgestão. - Uso fluente de ferramentas de produtividade técnica (Claude Code, Cursor, Copilot, agentes de coding) para acelerar entrega e destravar decisões. - Comunicação técnica proativa: comunica blockers, trade-offs e mudanças de direção cedo e por escrito, sem precisar ser provocado. - Postura proativa na identificação de problemas — capacidade de antecipar o que precisa ser feito antes de ser demandado, em vez de aguardar atribuição. - Convicção técnica baseada em evidência — defende approaches arquiteturais com clareza e revisita decisões apenas mediante dado novo que justifique a mudança. - Domínio técnico consolidado do estado da arte — chega em discussões de arquitetura com posição formada sobre decisões fundamentais do domínio (escolha de banco vetorial, fine-tuning vs. RAG, framework de eval, etc.). - Atuação como sparring técnico para liderança e pares de engenharia, sendo referência ativa em decisões de IA — e não o contrário. - Uso fluente de ferramentas modernas de produtividade técnica (Claude Code, Cursor, Copilot, agentes de coding) para destravar a si e ao time. Requirements - Experiência com arquiteturas agênticas avançadas: multi-agent debate, DAGs de agentes. - Defende approaches técnicos com convicção e evidência. - Experiência comprovada em GCP e/ou AWS (Vertex AI, BigQuery, Cloud Functions, Bedrock, SageMaker). - Deploys de modelos e agentes via Cloud Run, Vertex Endpoints ou Bedrock Agents. - Experiência com modelos open-source self-hosted (Llama, Qwen, DeepSeek, Mistral) e infraestrutura de inference (vLLM, TGI, Ollama). - Pipelines de geração e curadoria de datasets sintéticos para fine-tuning. - Experiência prévia liderando ou influenciando tecnicamente squads de IA/ML. - Certificações em IA, Cloud (GCP ML Engineer, AWS ML Specialty) ou áreas correlatas. Benefits - Política de Equipamentos: Bring Your Own Device (BYOD). - Seguem o calendário nacional de feriados no Brasil. Todos os feriados e emendas são respeitados. - Férias coletivas de Natal e Ano Novo +1 semana de férias a ser escolhida durante os 12 meses de contrato.

Role Description As the company expands into the United Kingdom, they are seeking a Forward Deployed AI Engineer to work directly with customers, deploy AI-powered solutions, improve operational processes, and drive measurable business outcomes. This is a highly strategic hire and one of the company's top priorities as they establish their UK presence. Key Responsibilities - Partner directly with customers from implementation through expansion - Analyze and map operational workflows - Identify opportunities for automation and process improvement - Redesign inefficient processes before automation - Configure and deploy AI agents into customer environments - Translate business requirements into agent logic and decision frameworks - Build integrations using APIs, webhooks, SQL, Python, JavaScript, and enterprise systems - Develop scripts, prototypes, and workflow automations - Troubleshoot deployments and optimize production performance - Lead customer workshops and working sessions - Build trusted relationships with technical and business stakeholders - Create reusable implementation assets, templates, and deployment frameworks - Collaborate closely with Product and Engineering teams to improve future deployments Qualifications - 5+ years of experience in customer-facing technical roles - Proven experience deploying AI or automation solutions into real customer environments - Background in; Forward Deployed Engineering, Solutions Engineering, Applied AI Engineering, Technical Consulting, Enterprise Automation Delivery - Hands-on experience with; LLMs, Agent frameworks, Prompt engineering, Retrieval systems, AI deployment workflows - Strong experience with; SQL, Python and/or JavaScript, APIs, JSON, Webhooks, Enterprise integrations - Willingness to travel to Europe occasionally Nice to Have - Experience at AI-native companies - Previous Forward Deployed Engineering or Solutions Engineering experience - ERP implementation or integration experience - Technical consulting experience focused on AI or automation - Additional European language skills - Experience building internal AI-powered operational tools Benefits - Competitive compensation package - Meaningful equity participation - Opportunity to help establish the company's UK presence - Direct customer and product impact - High-autonomy and high-ownership environment - Exposure to cutting-edge AI technologies - Fast-moving and entrepreneurial culture

Role Description AI Architect is a technical leader reporting to the Director, Enterprise App Engineering. This role defines and drives Guidewire's enterprise AI strategy — translating business objectives across GTM, Finance, HR, Legal, and Engineering into production-grade AI systems that deliver measurable impact. The architect owns end-to-end design across ingestion, orchestration, agent frameworks, security, compliance, and delivery. - Define and own Guidewire's enterprise AI architecture vision — establishing technical direction, design standards, and reference implementations across all BizTech AI initiatives. - Architect and lead delivery of production AI systems: multi-agent orchestration platforms, RAG and GraphRAG knowledge systems, LLM-powered workflows, and enterprise context graph initiatives spanning all Systems of Record. - Own the full-stack AI architecture — from AWS infrastructure and data pipelines (Kafka, Iceberg, dbt, pgvector) through LLM gateway design, agent orchestration (LangGraph or equivalent), and user interfaces. - Help define enterprise AI security and governance standards — covering IAM, ABAC policy enforcement, PII handling, prompt injection defence, hallucination detection, and audit trail design for SOX and GDPR compliance. - Lead complex, multi-team AI programs end to end — from stakeholder alignment and architectural design through delivery, adoption, and measured business outcomes across concurrent initiatives. - Help drive technology evaluation and build-vs-buy decisions across the AI tooling portfolio. - Partner with Data Governance, Security, Legal, and Compliance teams to embed AI responsibly across the organisation. - Mentor senior engineers and foster a culture of technical excellence, pragmatic innovation, and accountable delivery across the BizTech organization. Qualifications - Bachelor's Degree in Computer Science, Engineering, or equivalent work experience. - 12+ years in enterprise software architecture, with at least 2 years designing and delivering production AI/ML or LLM-based systems at scale. - Proven track record leading large, complex enterprise AI programs — multi-year, multi-team initiatives with clear, measurable business outcomes. - AWS expertise: Bedrock, EKS, Lambda, RDS, S3, Kinesis, IAM, VPC. AWS Solutions. - Full-stack AI fluency: event ingestion and streaming pipelines, vector and graph databases, LLM orchestration and agent frameworks, API design, and front-end delivery. - Experience with AI observability and evaluation — trace instrumentation, eval frameworks, and production monitoring (Datadog LLM Observability, LangSmith, or equivalent). - iPaaS and integration architecture experience — Workato, MuleSoft, or equivalent for enterprise SoR integration and event-driven automation. - 5+ years of technical leadership — mentoring senior engineers, driving architecture strategy, and managing cross-functional stakeholder relationships. - Excellent communication and executive presence — able to present complex AI architecture decisions clearly to both engineering teams and senior business leadership. - Demonstrated experience shipping Generative AI systems in production enterprise environments, with measurable impact on business outcomes. Requirements - The US base salary range for this full-time position is $132,000 - $198,000. Your base pay will depend on your experience, skills, education, training, and location among other factors. - All full-time positions or part-time roles working 30 hours or more a week at Guidewire are eligible for benefits that support their health and well-being including health, dental, and vision insurance, paid time off, and a company sponsored retirement plan. - In addition, some roles may be eligible for the annual company bonus plan, commissions, and/or long term incentive awards which are contingent on a variety of factors including, but not limited to, company and employee performance. Benefits - Disability Accommodations and Guidewire’s Appeals Process. Guidewire provides accommodations to the hiring process to create a fair opportunity for candidates with disabilities to contend for open positions. - Accommodation requests should be directed to Accommodations@guidewire.com. - If things do not go as hoped, we invite you to use our appeals process. Guidewire promises to independently review any denied accommodation and any decision not to offer you the position. - The appeals process is the same in either case. Within five business days of receiving a notice of denial of an accommodation, or receiving a notice of your non-selection for a vacancy, e-mail Accommodations@guidewire.com to make an appeal. - Guidewire will assign a new decision-maker to review the request and/or hiring decision, who will then notify you in writing of a decision within 10 business days.

Role Description A fast-growing cloud-native professional services company is hiring Forward Deployed Engineers for a new AI-focused business unit built around enterprise LLM adoption. This is a startup-style team inside a larger, established consulting organization: small team, high autonomy, direct client ownership, and real delivery responsibility from day one. You’ll work directly with enterprise customers to take AI from idea to production. Engagements may focus on AI-assisted software development, agentic engineering workflows, RAG/LLM applications, or applied AI enablement for business teams. - Own client engagements end-to-end: discovery, scoping, architecture, delivery, enablement, and expansion. - Run stakeholder interviews, technical workshops, architecture reviews, and enablement sessions. - Build or guide production AI systems such as agents, RAG pipelines, AI-native apps, workflow automations, or AI-assisted SDLC tooling. - Translate ambiguous business problems into practical, deliverable technical solutions. - Work with both technical and non-technical stakeholders, from engineers and architects to VP/C-suite leaders. - Identify opportunities to expand accounts through strong delivery and proactive problem-solving. - Leave clients with durable capability: documented, maintainable systems, workflows, and enablement materials. Qualifications - 4–10 years of experience in client-facing technical delivery, consulting, systems integration, professional services, FDE, solutions engineering, or customer-facing software engineering. - Hands-on experience with LLM applications, AI agents, RAG pipelines, AI workflows, or AI-assisted development tools. - Comfortable with tools such as Claude, Claude Code, Cursor, Codex, AWS Bedrock, LangChain/LangSmith, Python, AWS, Kubernetes, Zapier, Power Automate, or similar. - Strong consulting instincts: able to run discovery, manage stakeholders, communicate clearly, and drive outcomes without heavy oversight. - Able to speak technically with engineers while also explaining business value to executives. - High tolerance for ambiguity and a bias toward action. Requirements - Two strong-fit profiles: - AI/Workflow Transformation FDE: Consulting, implementation, or enterprise SaaS delivery background with hands-on AI enablement experience. Strong fit for candidates who have helped business teams adopt AI tools, automate workflows, or transform knowledge-worker processes. - Technical / SDLC FDE: Software engineering background with client-facing delivery experience. Strong fit for candidates who have built production AI systems, agentic coding workflows, developer tooling, RAG apps, or engineering enablement programs. - Strong signals: - Prior consulting, professional services, systems integration, or implementation experience. - Experience leading workshops, enablement sessions, or technical discovery. - AWS/cloud consulting background. - Production AI/LLM project experience. - Account expansion or “sell-do” delivery experience. - Comfort working remotely with up to 50% travel depending on client needs.