Head of AI Solutions & Delivery

About TVARIT:
TVARIT GmbH specializes in developing and delivering cutting-edge artificial intelligence (AI)  solutions for the metal industry, including steel, aluminum, copper, cast iron, and more. Our software products empower customers to make intelligent, data-driven decisions, driving advancements in Predictive Quality (PsQ), Predictive Maintenance (PdM), and Energy Consumption Reduction (PsE).
With a strong portfolio of renowned reference customers, state-of-the-art technology, a talented research team from prestigious universities, and recognition through esteemed awards such as the EU Horizon 2020 AI Prize, TVARIT is recognized as one of the most innovative AI companies in Germany and Europe.
We are looking for a self-motivated person with a positive “can-do” attitude and excellent oral and written communication skills in English.
About the Role:
We are looking for a senior leader to head TVARIT’s Metals Division, owning customer  relationships, solution delivery, and the growth of our industrial AI business with steel, aluminum, copper, cast iron, and other metal producers and processors. The role calls for an engineer who combines first-hand manufacturing plant experience, ideally in metals, with strong data science and AI expertise and a proven record of leading software delivery with a team
The Head of AI Solutions & Delivery will be the senior face of TVARIT to metals customers, from the first discovery workshop through deployment and scaled adoption, and will be accountable for delivering projects on time, within scope, and with measurable impact on quality, yield, uptime, and energy consumption. The role leads a multidisciplinary team of data scientists, software engineers, and domain experts, and turns successful deployments into reusable, scalable offerings for the metals sector.
Key Responsibilities
Division Leadership & Growth:
• Lead the Metals Division end to end, including strategy, customer portfolio, revenue and delivery targets, and division-level planning and reporting. • Build, lead, and mentor a multidisciplinary team of data scientists, ML engineers, software engineers, and metallurgy/process domain experts; set clear goals, hiring plans, and standards of technical excellence. • Define the division’s solution and product roadmap together with product and business teams, prioritizing use cases with recurring demand across steel, aluminum, and other metal processes. • Grow existing accounts and win new business through solution design, technical proposals, RFP/RFI responses, proof-of-concept planning, and commercial discussions.
Customer Engagement: • Act as the senior technical and executive point of contact for metals customers, building trusted relationships with plant managers, process and quality engineers, IT/OT teams, and senior decision makers. • Lead discovery discussions and requirements workshops, on the shop floor and in the boardroom, to understand manufacturing processes, operational challenges, existing systems, and business priorities. • Translate customer needs into clearly defined use cases, solution concepts, highlevel architectures, implementation scope, and measurable success criteria. • Present TVARIT’s capabilities through relevant project examples, demonstrations, and workshops, communicating methodology, technical differentiation, limitations, and expected business value credibly to technical and executive audiences
Delivery Excellence: • Own delivery of all Metals Division projects, from scoping and data onboarding  through model development, deployment, user adoption, and value realization, ensuring on-time, on-budget, high-quality outcomes. • Establish delivery governance across projects: plans and milestones, risk and dependency management, customer status reporting, and escalation handling. • Assess feasibility before commitment, considering data readiness, integration with PLC/SCADA, MES, and historians, deployment constraints, effort, and business impact; align customer commitments with team capacity and technical readiness. • Track and demonstrate realized value (e.g., scrap reduction, yield, first-pass quality, energy per tonne, unplanned downtime) and use proven results to drive renewals and expansion.
Technical, Data Science & AI Leadership: • Provide hands-on technical direction on data science and ML approaches for metal processes, including time-series modeling, predictive quality, predictive maintenance, process optimization, and hybrid physics-informed models. • Review solution architectures and engineering practices for robustness, scalability, and maintainability; set standards for software delivery, MLOps, and model monitoring. • Identify reusable intellectual property across projects, including models, algorithms, software components, and process expertise, and guide its development into configurable, scalable products for the metals sector with minimal incremental development where feasible. • Stay current on industrial AI, including generative AI and industrial copilots, and evaluate where they add real value for metals customers.
Qualifications:
• Bachelor’s or master’s degree in Engineering (Metallurgical, Mechanical, Electrical, Chemical, Computer Science, or a related discipline); a postgraduate qualification in Data Science, AI, or Computer Science is an advantage. • 10+ years of professional experience, including substantial experience leading teams and customer engagements in industrial AI, manufacturing technology, industrial software, or technology-led solution delivery. • Mandatory manufacturing exposure: direct experience working in or closely with manufacturing plants and their operations teams. Metals experience (e.g., steel, aluminum, copper, foundry/casting, rolling, forging, extrusion, or heat treatment) is strongly preferred; automotive, heavy engineering, or process manufacturing is also considered • Software delivery leadership: must have led the delivery of software products or solutions with a team, with a solid working understanding of the software development lifecycle (requirements, design, development, testing, release, and maintenance) and agile delivery practices. Hands-on coding exposure (e.g., Python) is mandatory; professional software development experience is an advantage. • Mandatory AI modeling experience: strong understanding of AI/ML modeling (problem framing, feature engineering, model selection, validation, and deployment considerations) and hands-on involvement in AI/ML modeling projects, ideally on industrial, sensor, or time-series data. • Proven customer-facing leadership , including requirements gathering, technical presentations, executive stakeholder management, and managing customer expectations through delivery • Proven delivery track record: leading complex, multi-stakeholder technology projects to successful deployment, with accountability for timelines, scope, quality, and customer satisfaction. • Experience in solution architecture, presales, technical proposals, or productizing project work into reusable offerings. • Strong people leadership, with experience building and mentoring high-performing technical teams; excellent written and verbal communication, structured problemsolving, and the ability to represent TVARIT confidently with senior stakeholders. • Willingness to travel to customer plants as required.
Technical Skills
Strong Haves • Data science and AI: Strong understanding of machine learning, deep learning, time-series analysis, anomaly detection, predictive and prescriptive analytics, optimization, and hybrid physics-informed modeling, with a clear view of their practical applications and limitations. • Metals process understanding: Ability to relate process parameters in melting, casting, rolling, forging, extrusion, or heat treatment to quality, yield, scrap, energy consumption, equipment health, and operating cost. • Software development lifecycle: Working understanding of how industrial software is planned, built, tested, released, and supported, including agile practices, CI/CD, code quality, and testing, sufficient to lead engineering teams and make sound delivery trade-offs. Hands-on coding exposure in Python is required, along with working familiarity with SQL. • Solution architecture and productization: Translating business and operational requirements into functional specifications, reference architectures, and implementation roadmaps; reusable components, configurable solutions, model generalization, and validation across plants. Familiarity Needed • Industrial data and integration: Familiarity with PLC/SCADA systems, historians, MES, sensors, lab and quality data, and manufacturing data platforms, including data quality, contextualization, and OT/IT integration • Deployment and MLOps: Cloud, edge, and on-premises deployment, MLOps, model monitoring and retraining, security, and reliability. Familiarity with Azure is preferred; AWS or GCP experience is also relevant. • Emerging AI: Awareness of generative AI, LLMs, and industrial copilots, including their practical applications and limitations in manufacturing