AI Enablement Lead [gn] Data Intelligence

Actian · Europe - RemoteRemotosenior

En corto

▸Líder técnico que construye plataformas de IA para que todos los equipos de Actian usen IA con seguridad y escalabilidad.
▸Día a día: crea herramientas de IA, integra IA en productos, supervisa rendimiento, costo y gobernanza, y capacita a ingenieros no especializados.
▸Destacado: no es un investigador teórico, sino un ingeniero práctico que impulsa la IA en toda la organización con infraestructura real y código de producción.

Exceptional verbal and written English communication skills.

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¿Qué piden?

✓Experiencia como ingeniero senior de IA/ML o arquitecto de software con productos empresariales.
✓Conocimiento profundo de Python o Go, bases de datos vectoriales y frameworks de orquestación (LangChain, LlamaIndex).
✓Habilidades en LLMOps, monitoreo de modelos, gobernanza de IA y optimización de costo y latencia.
✓Capacidad para liderar sin autoridad: influir en equipos técnicos sin ser su jefe directo.
✓Experiencia en CI/CD, contenedores (Docker/Kubernetes) y pruebas de modelos (conjuntos de evaluación).
✓Excelente comunicación en inglés (oral y escrito) para traducir técnicas complejas a valor de negocio.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

PythonGoLangChainLlamaIndexPineconeMilvuspgvectorDockerKubernetesCI/CD

¿A quién escribirle en Actian?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

The AI Enablement Team is the catalyst for internal transformation and product acceleration across Actian. In the modern data landscape, AI is not a siloed experimental lab; it is a core capability that must be embedded into our product DNA and our engineering workflows. We are looking for an AI Enablement Lead who will architect, scale, and own our AI enablement strategy end-to-end. You are not a theoretical researcher or a passive prompt engineer; you are a highly practical, technical driver who builds the foundational platforms, tooling, and frameworks that allow other product and engineering teams to deploy AI safely, rapidly, and at scale. You will democratize AI across the organization, establish modern LLMOps/MLOps practices, and directly impact the Actian Data Intelligence Platform by introducing agentic workflows, intelligent data pipelines, and cutting-edge capabilities. , advanced RAG architectures, agentic frameworks) for company-wide consumption. Product AI Integration: Collaborate directly with core engineering teams to embed production-ready generative AI and machine learning features into the Actian Data Intelligence Platform. LLMOps & Governance Infrastructure: Establish strict guardrails, evaluation frameworks, and monitoring tools to track model performance, bias, data privacy, and security across all AI implementations. Cost & Latency Optimization: Actively monitor and manage cloud and API compute spend (token management, open-source vs. commercial models) and optimize execution latency for production AI features. Cross-Functional Upskilling: Lead workshops, design blueprints, and create documentation to empower non-AI engineering teams to build and maintain their own AI-driven features confidently. Rapid Prototyping (PoC to Production): Drive the engineering execution of high-impact AI proof-of-concepts, ensuring they are built with production-grade code that scales seamlessly. Standardization of Tooling: Define and enforce the organization's official AI stack, from vector database selection and vector embeddings strategies to semantic caching mechanisms. Vendor & Open-Source Strategy: Evaluate and manage partnerships with AI model providers and lead the technical assessment of cutting-edge open-source models to keep Actian at the vanguard of innovation. Data-Driven Impact Tracking: Define and track operational metrics for the AI Enablement function, such as developer adoption rates, reduction in time-to-market for AI features, and ROI of implemented AI tools. Qualifications & Profile: Technical Background: Strong background as a Senior AI/ML Engineer, LLMOps Engineer, or Software Architect who has successfully built and scaled AI-powered applications in enterprise SaaS or complex data platforms. , Pinecone, Milvus, pgvector), orchestration frameworks (LangChain, LlamaIndex), and fine-tuning or prompt engineering of state-of-the-art Large Language Models (LLMs). Extreme Ownership: High-agency mindset. You don’t wait for product teams to ask for AI capabilities; you proactively build the frameworks that solve their bottlenecks before they even identify them. Software Engineering Rigor: You treat AI development as software engineering. You understand CI/CD, unit testing for AI (evaluation datasets), containerization (Docker/Kubernetes), and clean code architecture. Influence Without Authority: Exceptional leadership and communication skills. You can inspire and align disparate engineering teams around a shared technical vision without being their direct line manager. Communication: Exceptional verbal and written English communication skills.

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