Generative AI Engineer

Agentic AI Solution Development & Integration

• Design end-to-end AI solutions on Dataiku's platform, leveraging Dataiku Agent Hub, Prompt Studio, LLM Mesh, and Knowledge Banks (Vector Stores), or Python-based frameworks where needed.
• Build and orchestrate multi-agent systems using Dataiku's Visual Agents, as well as code-based frameworks (LangGraph, CrewAI, Claude Agent SDK, OpenAI Agents SDK).
• Integrate and optimize LLM APIs across providers (OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure, open-source models), applying model routing strategies.
• Implement Retrieval-Augmented Generation (RAG) pipelines, including agentic RAG and GraphRAG, using Dataiku's Knowledge Banks with reranking, dynamic filtering, and document extraction.

Stakeholder Engagement & Delivery

• Work exclusively with the Marketing organisation, partnering across Demand Generation, Content Marketing, Product Marketing, Field Marketing, Marketing Operations, Brand, and Communications.
• Engage marketing stakeholders to gather business requirements, identify underlying pain points, and design solutions addressing both stated needs and deeper problems.
• Own projects end-to-end, from requirements intake and solution design through to build, deployment, and handover.

Agent & Tool Development

• Develop autonomous and semi-autonomous AI agents using Dataiku's Agent Builder, custom Python-based architectures (LangGraph, CrewAI, Claude Agent SDK), or combinations.
• Design and build Agent Tools beyond documented examples, including custom API integrations, data retrieval modules, decisioning logic, and automated workflows.
• Build, publish, and consume MCP (Model Context Protocol) servers to enable agent-to-tool integration across systems.
• Develop evaluation and monitoring approaches for agent systems to measure reliability, accuracy, cost, and business impact.

AI Governance & Evaluation

• Design and maintain evaluation frameworks (evals) for LLM-based systems, measuring accuracy, latency, cost, and reliability.
• Adhere to data governance, security, and regulatory compliance requirements (EU AI Act awareness, responsible AI practices).
• Leverage Dataiku's Cost Guard and Quality Guard features to manage LLM spend and enforce usage policies.
• Work with analytics and data engineering teams to maintain metadata on reference datasets for LLM consumption.

Web Application Development

• Create front-end user interfaces for AI applications using HTML, CSS, and JavaScript within Dataiku's webapps framework or standalone applications built with Vue.js and Node.js.
• Collaborate on UX design ensuring internal stakeholders find AI solutions intuitive and responsive.

Continuous Learning

• Provide product feedback to the development team.
• Stay current with the evolving AI engineering landscape, agent frameworks, model capabilities, evaluation practices, governance requirements, and tools like MCP and A2A protocols.

Technical Proficiency Requirements

• Strong Python skills including familiarity with data science and AI engineering libraries.
• Hands-on experience building agentic AI systems, multi-agent orchestration, tool chaining, autonomous decision-making, and production deployment of AI agents.
• Experience with Go-to-Market motions and tools including Salesforce, HubSpot, 6sense, Gong, Outreach.
• Experience with modern agent orchestration frameworks (LangGraph, CrewAI, Claude Agent SDK, OpenAI Agents SDK).
• Understanding of RAG architectures (vector databases, embedding strategies, agentic RAG, GraphRAG).
• Familiarity with MCP (Model Context Protocol) for agent-to-tool integration.
• Experience with structured outputs, function/tool calling, and prompt engineering across multiple LLM providers.
• Web development fundamentals (HTML, CSS, JavaScript); Vue.js and Node.js preferred.
• Exposure to AI evaluation practices, building evals, monitoring model/agent performance in production.
• Comfort with AI-assisted development tools (GitHub Copilot, Cursor, Claude Code).

Soft Skills Requirements

• Strong communication and presentation skills for collaborating with technical and non-technical stakeholders.
• Problem-solving mindset with passion for innovation and delivering measurable business value.
• Openness to learning new tools and adapting to rapidly evolving AI landscape.