Role Description Our client is a European Deep-Tech AI software company that has spent the last several years building a technically ambitious platform for modeling, analysis, and decision-making in complex systems. The challenge now is turning a fast-growing body of technical work into a consistently delivered, well-sequenced system. You will work across research, core engineering, infrastructure, and product-facing components. This is an Engineering Lead Position for someone who understands complex software systems and knows how to make teams ship coherently - without needing formal authority or people management. It is a hands-on, technical delivery role focused on making sure the right work happens at the right time - across teams - without relying on formal authority. What you’ll do: - Own the technical delivery roadmap, translating inputs from research, product needs, technical debt, and infrastructure into a coherent and realistic quarterly plan. - Lead quarterly planning and ongoing execution tracking across multiple technical domains (core systems, infrastructure, frontend, and related areas). - Maintain and continuously refine the task structure and dependency map - flag outdated priorities, inconsistencies, and missing ownership early. - Act as a cross-team connector, ensuring engineers working on different parts of the system stay aligned and releases converge cleanly. - Support release coordination, reducing risk from parallel work and preventing last-minute surprises. - Take ownership of technical documentation standards - contributing directly where needed and keeping knowledge usable and current. - Improve how engineering work gets done by introducing practical process improvements and lightweight tooling where it clearly adds value. - Be hands-on when needed – from improving internal tooling to unblocking technical work – with flexibility depending on your strengths and the team’s needs. The split between hands-on technical work and coordination is intentionally flexible and will depend on the individual. What this role is not: - Not a Scrum Master or ceremony-focused role. - Not a delivery-only PM who just tracks tickets. - Not a people manager with direct reports. - Not a consulting-style project role. This role exists to own execution, not to enforce process or manage headcount. Qualifications - Strong background in software engineering - you can engage with engineers as a peer and understand technical tradeoffs. - Proven experience owning delivery across teams, even without formal authority. - Comfortable operating in environments where structure evolves and priorities shift. - Experience working in high-standard software engineering organizations with disciplined execution, planning, and release practices. - Naturally proactive: you spot risks early, connect the right people, and keep work moving. - Clear communicator with low ego – focused on outcomes rather than ownership. - Bonus: experience improving engineering workflows, internal tooling, or release coordination in complex systems. Benefits - Work at the center of a technically ambitious, frontier AI platform. - High autonomy and direct collaboration with senior technical leadership. - Collaborate with a team of scientists and engineers & PhDs - in an exciting, fast-moving environment. - 100% REMOTE within Europe, with frequent team retreats. - Compensation package of up to 150k EUR (including Bonuses & Equity). - The chance to shape how a complex engineering organization delivers at scale. Company Description Baldur Connect is acting as an Employment Agency in relation to this vacancy. Baldur Connect is committed to equal opportunity and encourages applications from all qualified candidates, regardless of background.

• Own the full machine learning lifecycle from research to production • Leverage Computer Vision and NLP techniques (e.g. BERT, ViT) to extract insights from unstructured data • Advocate for sustainable tradeoffs, architecting solutions that balance product needs and long term goals • Design experiments to quantify the business impact of machine learning models • Collaborate with other MLEs on tooling improvements for model training and deployment • Write robust, production-grade Python code to productionalize feature engineering, model training, and deployment • Participate in the on-call rotation to assist engineers in troubleshooting

• Opportunity discovery & prioritization: Run process discovery with supply chain teams (planning, scheduling, inventory, procurement, logistics) to map and size AI opportunities; build a sequenced backlog tied to measurable outcomes. • Hands-on POCs to MVPs: Rapidly build POCs and evolve the best into MVPs using AWS + Databricks + Dataiku + OpenAI, integrating with enterprise data sources and real workflows. • Roadmap ownership: Produce and maintain a capability roadmap (90 days/6 months/12 months) with dependencies, data readiness needs, and adoption/change requirements. • Kinaxis-enabled use cases: Design solutions that complement and enhance Kinaxis workflows (exception management, scenario narratives, decision support, insight generation, planning explainability). • Balance quick wins vs foundations: Deliver short cycle wins that work today while shaping requirements for scalable infrastructure as IT builds out data/AI foundations (reusable components, standard integration patterns, agent orchestration approaches, monitoring/evaluation patterns). • Vision and transition plan: Define a north-star vision and pragmatic transition plan bridging today's constraints to the medium/long-term target state (platform, governance, integration patterns, reusable AI assets). • Production-minded engineering: Define architecture, evaluation metrics, guardrails, monitoring, and last-mile integration patterns; partner with platform/data teams while remaining personally hands-on. • Drive ChatGPT adoption: Build role-based prompt libraries, playbooks, templates, and enablement sessions; measure adoption and iterate based on usage and impact. • Stay current and apply responsibly: Maintain working knowledge of the rapidly evolving AI landscape (GenAI, agents, evaluation, toolchains) and translate relevant advances into practical solutions aligned to enterprise guardrails.

• Map DPD opportunities for AI: Identify and size use cases across drug product operations (execution insights, investigations support, batch record intelligence, documentation acceleration, knowledge retrieval, exception management, production performance). • Build and ship solutions: Deliver POCs/MVPs hands-on - integrating GenAI with operational data and controlled documentation in secure, controlled, and adoption-ready ways. • Roadmap and value cases: Own a DPD AI roadmap with value sizing, dependencies, and adoption plans; align priorities with DPD stakeholders. • Balance quick wins vs foundations: Build what is needed now while shaping and standardizing patterns that will scale with evolving enterprise data/AI infrastructure (reusable components, integration standards, evaluation/monitoring, agent orchestration patterns). • Vision and transition plan: Define the DPD AI north star and a staged plan from POCs to reusable capabilities to scaled solutions as infrastructure matures. • Quality-aware design: Build with regulated realities in mind (traceability expectations, controlled content boundaries, auditability of outputs where required). • Drive ChatGPT adoption: Create role-based prompt kits, playbooks, SOP-adjacent guidance, and enablement sessions; measure adoption and iterate. • Stay current and apply responsibly: Continuously monitor the rapidly advancing AI landscape and selectively bring in relevant capabilities that are safe, practical, and measurable in DPD operations.