Role Description Estamos em busca de um(a) Arquiteto(a) de IA Pleno para atuar na concepção, desenvolvimento e implementação de soluções baseadas em Inteligência Artificial, com foco em IA Generativa, Machine Learning e integração de sistemas. Este profissional participará da definição e evolução de arquiteturas de IA, apoiando a construção de soluções escaláveis, seguras e alinhadas às necessidades do negócio. - Apoiar o desenho e a implementação de arquiteturas de soluções baseadas em Inteligência Artificial e IA Generativa. - Levantar e traduzir requisitos de negócio em soluções técnicas utilizando IA, automação e análise de dados. - Desenvolver e implementar soluções utilizando LLMs, RAG (Retrieval-Augmented Generation), agentes de IA e integrações com sistemas corporativos. - Construir e disponibilizar modelos e serviços de IA por meio de APIs e microsserviços. - Atuar na implementação de soluções em plataformas de IA em nuvem, garantindo desempenho, segurança e governança. - Participar da definição e implementação de arquiteturas de dados para aplicações de IA, incluindo Data Lakes, Data Warehouses e bancos vetoriais. - Apoiar a realização de provas de conceito (PoCs), testes técnicos e avaliações de novas tecnologias. - Aplicar boas práticas de desenvolvimento, governança, segurança e uso responsável da Inteligência Artificial. - Colaborar com equipes multidisciplinares de dados, desenvolvimento e negócios para garantir a integração adequada das soluções. - Apoiar o monitoramento, manutenção e evolução contínua das soluções implantadas utilizando práticas de MLOps. - Produzir documentação técnica, diagramas de arquitetura e especificações funcionais e técnicas. - Participar de reuniões técnicas com clientes e stakeholders para entendimento de necessidades e apresentação de soluções. Qualifications - Experiência em projetos de Inteligência Artificial, Machine Learning e/ou IA Generativa. - Conhecimento em LLMs (Large Language Models), Prompt Engineering, RAG e agentes de IA. - Experiência com plataformas de IA em nuvem, preferencialmente Microsoft Azure (Azure AI, Azure OpenAI, AI Foundry), AWS ou Google Cloud. - Conhecimento em arquitetura de integração, APIs REST e microsserviços. - Experiência com Python e SQL. - Conhecimento em bancos de dados relacionais, Data Lakes, Data Warehouses e bancos vetoriais. - Familiaridade com práticas de MLOps, DevOps, CI/CD e monitoramento de aplicações. - Conhecimento em governança, segurança da informação e privacidade de dados. - Capacidade de compreender requisitos de negócio e transformá-los em soluções técnicas. - Inglês técnico para leitura de documentação e pesquisa. Competências - Pensamento analítico e capacidade de resolução de problemas. - Visão sistêmica para compreender fluxos de dados e integrações complexas. - Boa comunicação verbal e escrita. - Facilidade para trabalhar em equipes multidisciplinares. - Perfil colaborativo e orientado ao compartilhamento de conhecimento. - Proatividade na identificação de melhorias e oportunidades de inovação. - Organização e capacidade de gerenciar múltiplas demandas. - Adaptabilidade frente à evolução constante das tecnologias de IA. - Foco em qualidade, segurança e entrega de valor ao negócio. Diferenciais - Experiência com Azure OpenAI, AI Foundry, Copilot Studio e Power Platform. - Certificações Microsoft, AWS ou Google relacionadas à IA, Dados ou Cloud. - Experiência em projetos de automação inteligente, análise de documentos ou assistentes virtuais. - Conhecimento em frameworks como TensorFlow, PyTorch, LangChain, Semantic Kernel ou similares. - Experiência em projetos na área de saúde, financeiro ou outros setores regulados. - Conhecimento em bancos vetoriais como Azure AI Search, Pinecone, Weaviate ou Chroma. - Participação em comunidades, eventos ou iniciativas relacionadas à Inteligência Artificial.

• Lead and oversee the AI team in designing, developing, and deploying AI and machine learning models and solutions. • Define and execute the AI strategy aligned with business goals and emerging industry trends. • Collaborate with cross-functional teams including data science, engineering, product management, and business units to identify AI-driven opportunities and deliver impactful solutions. • Manage AI projects ensuring timely delivery, quality, and alignment with organizational objectives. • Mentor and develop the AI team by fostering skills growth, collaboration, and innovative thinking. • Stay abreast of the latest AI research, tools, and methodologies and assess their applicability to business problems. • Communicate complex AI concepts clearly to non-technical stakeholders to facilitate informed decision-making. • Advocate best practices for AI ethics, data privacy, and security within the organization.

- Lead and mentor a cross-functional engineering team, fostering a culture of collaboration, innovation, and continuous improvement, with a strong focus on AI adoption. - Drive the design, implementation, and evolution of AI-powered solutions, particularly AI agents, to enhance software development workflows, developer productivity, and operational efficiency. - Act as a hands-on technical leader, actively contributing to system design, code reviews, and the development of AI-driven capabilities integrated into the platform. - Identify and prioritize high-impact opportunities to leverage AI across the software development lifecycle, including code generation, testing, debugging, incident analysis, and documentation. - Architect and implement scalable and secure systems that combine traditional backend engineering with AI components (e.g., LLM integrations, RAG pipelines, agent-based workflows). - Collaborate with product, engineering, and business stakeholders to define AI use cases aligned with company goals and deliver measurable impact. - Establish best practices for building, evaluating, and maintaining AI systems in production, including observability, performance, cost control, and reliability. - Drive the adoption of AI tools and practices across engineering teams, acting as a mentor and enabler for developers to effectively leverage AI in their daily work. - Ensure that AI implementations comply with security and regulatory requirements (e.g., PCI DSS, GDPR, DORA), especially in the context of sensitive financial data. - Mitigate technical risks related to both traditional systems and AI components, ensuring platform stability, resilience, and trustworthiness. - Contribute directly to the development of high-quality, high-performance software solutions, maintaining a strong hands-on involvement. - Diagnose and resolve complex issues in production environments, including those involving AI systems and agent-based workflows. - Drive technical decision-making, balancing innovation in AI with long-term scalability, maintainability, and business impact. - Drive technical decision-making, balancing long-term scalability with immediate business needs. - Foster a culture of innovation, collaboration, and accountability across technical teams.

Role Description As an AI Engineer, you will design, develop, and deploy AI-driven solutions that solve real-world business challenges. You will work across the full AI lifecycle—from data preparation and model development to deployment, monitoring, and optimization—while collaborating closely with product managers, software engineers, and domain experts. This role is ideal for engineers with hands-on experience in machine learning and Generative AI who are passionate about building production-ready AI systems and delivering measurable business impact. AI Development & Deployment - Design, develop, and optimize machine learning and deep learning models for production environments. - Build AI-powered features and services that address customer and business needs. - Own the end-to-end AI lifecycle, including data exploration, model development, evaluation, deployment, monitoring, and continuous improvement. - Perform model evaluation, error analysis, and performance optimization to ensure reliability and accuracy. Generative AI & LLM Applications - Develop and enhance AI applications powered by Large Language Models (LLMs). - Build Retrieval-Augmented Generation (RAG) systems, semantic search solutions, and AI assistants. - Work with embeddings, vector databases, and modern AI orchestration frameworks. - Evaluate and improve model outputs for quality, relevance, latency, and user experience. Production Engineering - Deploy and maintain AI services in cloud and containerized environments. - Build scalable APIs and inference pipelines for real-time and batch processing workloads. - Monitor AI systems in production and troubleshoot performance or reliability issues. - Collaborate with engineering teams to integrate AI capabilities into customer-facing products. Collaboration & Innovation - Partner with Product, Engineering, Data, and Customer Success teams to translate business requirements into AI solutions. - Contribute to technical discussions, design reviews, and AI best practices. - Stay up to date with emerging AI technologies and recommend practical innovations that create business value. Qualifications - 2–4 years of professional experience in Artificial Intelligence, Machine Learning, Data Science, or related engineering roles. - Proven experience developing and deploying AI or machine learning solutions into production environments. Requirements - Strong proficiency in Python. - Experience with machine learning and deep learning frameworks such as PyTorch or TensorFlow. - Solid understanding of machine learning fundamentals, model evaluation techniques, and performance optimization. - Experience building AI applications using Large Language Models (LLMs). - Familiarity with Retrieval-Augmented Generation (RAG), embeddings, and vector search concepts. - Experience building APIs using FastAPI, Flask, or similar frameworks. - Understanding of software engineering best practices, version control, and testing methodologies. - Experience deploying AI models into production environments. - Familiarity with Docker and cloud platforms such as AWS, Azure, or GCP. - Understanding of monitoring, observability, and AI system reliability. - Strong analytical and problem-solving abilities. - Excellent communication and collaboration skills. - Ability to work effectively in a fast-paced, cross-functional environment. Preferred Qualifications - Experience with LangChain, LangGraph, LlamaIndex, or similar AI orchestration frameworks. - Experience working with vector databases such as Pinecone, Qdrant, ChromaDB, Weaviate, or FAISS. - Exposure to agent-based AI systems and workflow automation. - Experience with MLOps practices, CI/CD pipelines, and model monitoring. - Experience with computer vision, multimodal AI, recommendation systems, or NLP applications. - Familiarity with self-hosted open-source models and modern inference frameworks. What Success Looks Like - Deliver production-grade AI solutions that create measurable business impact. - Contribute to Lucidya's next generation of AI-powered products and capabilities. - Improve model performance, reliability, and scalability across AI services. - Collaborate effectively with engineering and product teams to bring AI innovations to market. - Continuously expand Lucidya's AI capabilities through practical experimentation and execution.