Role purpose:

The AI Tech Leader is accountable for defining and executing the AI technology strategy in alignment with Global AI architecture, standards and platforms. The role connects enterprise architecture, data foundations and business priorities to deliver secure, scalable and measurable AI solutions across Division. Acting as the technical authority for AI, the position will translate business needs into an actionable roadmap, guide use cases from ideation to industrialization, and ensure close coordination with Group AI, Business groups, GIT, Data, Cybersecurity and business teams. This role focuses on enterprise digital and data-driven AI capabilities rather than embedded product engineering.

Key skills and qualifications:

- AI architecture: Ability to define enterprise target architectures, reference patterns and integration approaches across cloud, data platforms, APIs, generative AI, machine learning, RAG, knowledge systems and AI agents.
- AI engineering: Strong command of Python, AI/ML frameworks, prompt and context engineering, model evaluation, MLOps/LLMOps, CI/CD, observability, testing and production support.
- Data foundations: Practical knowledge of data architecture, quality, lineage, metadata, access control, privacy, master data and secure integration of structured and unstructured sources.
- Strategy and portfolio management: Ability to convert business priorities into an AI roadmap and prioritize use cases against value, feasibility, data readiness, risk, scalability, adoption and total cost of ownership.
- Governance and risk: Working knowledge of responsible AI, cybersecurity, privacy, intellectual property, vendor risk and applicable regulation, including risk-based governance principles under the EU AI Act.
- Technical leadership: Ability to set standards, conduct architecture and design reviews, challenge suppliers, coach multidisciplinary teams and make clear technology decisions.

Leadership and collaboration skills:

- Cross-functional collaboration: Create effective ways of working across business functions, Data, GIT, Cybersecurity, Legal, Group AI and external partners, with explicit roles, decisions and escalation paths.
- Team enablement: Coach architects, engineers, data scientists and business contributors; promote knowledge sharing, reusable practices and continuous improvement across multidisciplinary teams.
- Change leadership: Build trust in AI, engage users early,



address adoption barriers and support business owners in embedding new solutions into processes and operating models.
- Decision-making and conflict resolution: Facilitate trade-offs between speed, value, risk, cost and technical sustainability, resolving disagreements through evidence-based and transparent decisions.

Key responsibilities:

- Understand global AI architecture, technology standards, governance model and approved platforms, and define how we will integrate with and leverage this ecosystem.
- Own the AI technology strategy and multi-year roadmap, balancing group reuse, division specific development and external solutions.
- Define target architecture, reference patterns and technology choices for generative AI, machine learning, AI agents, knowledge systems and intelligent automation.
- Partner with Data Architects, Data Engineers and Data Scientists to ensure that data quality, access, lineage, security and platform readiness support each AI solution.
- Work with business stakeholders to identify, assess and prioritize AI use cases based on strategic value, feasibility, data readiness, scalability, risk and expected return.
- Lead the technical lifecycle from discovery and proof of concept through pilot, production deployment, monitoring, adoption and continuous improvement.
- Establish engineering standards for solution design, APIs, reusable components, MLOps/LLMOps, testing, observability, documentation and cost management.
- Ensure compliance with cybersecurity, data privacy, responsible AI and applicable regulatory requirements throughout the solution lifecycle.
- Provide technical leadership, architecture reviews and coaching to AI and Data teams while coordinating internal experts, suppliers and technology partners.

AI governance and responsible AI:

- Apply risk-based qualification at the start of every use case, identify prohibited, high, limited and minimal-risk scenarios, and escalate required reviews to Legal, Cybersecurity, Data Privacy and the relevant technical governance bodies.
- Follow stage-gate controls from ideation through proof of concept, pilot, industrialization,



rollout and retirement, with documented go/no-go criteria and named accountable owners.
- Establish validation standards covering accuracy, relevance, hallucination, robustness, harmful bias, explainability, security, privacy, red-team testing and acceptance thresholds appropriate to each risk level.
- Ensure meaningful human oversight: AI may recommend or prepare actions, but accountable business owners retain decision and approval authority for material or sensitive outcomes.
- Require technical documentation, model and system records, traceable decisions, user information, limitations, approval evidence and audit logs throughout the AI lifecycle.
- Monitor production solutions for performance, drift, quality, incidents, misuse, access anomalies, operational cost and business impact; define escalation, rollback and decommissioning procedures.
- Promote AI literacy and responsible adoption by publishing guidance, training users and delivery teams, and reporting governance KPIs, risks, exceptions and remediation actions to management.

Required profile:

- Minimum five years of relevant experience in enterprise AI, machine learning, data platforms or AI solution architecture, including production deployments.
- Master’s degree in computer science, data science engineering, or a related discipline, or equivalent professional experience.
- Strong knowledge of enterprise AI architecture, cloud platforms, APIs, data integration and scalable deployment patterns.
- Hands-on understanding of generative AI, large language models, retrieval-augmented generation, AI agents, machine learning and model evaluation.
- Proficiency in Python and practical experience with common AI/ML frameworks; knowledge of MLOps/LLMOps, CI/CD, monitoring and cloud-native engineering.
- Experience translating business challenges into prioritized AI portfolios, technical architectures, delivery plans and measurable outcomes.
- Sound understanding of data governance, cybersecurity, privacy, responsible AI and regulatory compliance.
- Strong stakeholder management and communication skills, with the ability to influence technical teams, business leaders and group functions.
- Demonstrated technical leadership, structured problem solving, documentation discipline and ability to guide cross-functional teams without direct authority.
- Experience in automotive, industrial or regulated environments is an advantage.

📌 AI Leader - Digital Transformation (India)
🏢 Antal International
📍 India