Senior Manager - Enterprise Applications, AI & Automation

About the Role
This role focuses on accelerating the adoption of Generative AI, agentic automation, and advanced analytics across the enterprise application landscape, including ERP, Oracle, Salesforce, CRM, and Power platforms. You will design, develop, and deploy end-to-end automations utilizing machine learning and AI solutions to enhance operational efficiency, decision intelligence, and user experience across various business units such as Foods, Real Estate, Automotive, and Construction. The role demands working in an agile and iterative manner—rapidly prototyping use cases, validating them with business stakeholders, and scaling successful solutions into production. Collaboration with business teams to align AI initiatives with strategic priorities is essential, leveraging your strong data science, generative AI, and enterprise systems integration skills to assure success for the business, the project, and yourself.

Responsibilities
Strategic:

• Define and drive the AI/agentic automation roadmap for enterprise applications, aligned with the Group's digital transformation strategy.
• Identify high-impact use cases across business functions (Sales, After Sales, Manufacturing, Marketing, Supply Chain, Finance) where agentic AI, generative AI, and automation can create measurable value.
• Partner with business and IT leadership to prioritize AI/automation initiatives based on ROI, feasibility, and strategic fit.
• Evaluate emerging AI/ML and agentic frameworks, tools, and platforms (LLMs, RAG, vector databases, low-code AI orchestration) for enterprise adoption.
• Establish governance frameworks for responsible AI use, model risk management, and data privacy compliance across enterprise applications.
• Work with third-party vendors and technology partners to complete integrations of AI/automation solutions with D365, Oracle, Salesforce, and other enterprise systems.

Operational:

• Design, build, and deploy agentic AI workflows, chatbots, and automation pipelines integrated with enterprise systems (ERP, CRM, Oracle, Salesforce, Power Platform, BI tools).
• Develop and productionize ML/AI models (classification, forecasting, NLP, recommendation, RAG-based Q&A) using Python, XGBoost, MLflow, Databricks, OpenAI/Azure OpenAI, FAISS, or equivalent vector stores.
• Work with Data CoE to build and maintain dashboards and analytics solutions (e.g., Power BI) supporting data-driven decision-making across business units.
• Manage end-to-end project delivery—from requirement gathering and solution design through deployment, monitoring, and continuous optimization.
• Ensure integration of AI/automation solutions with existing enterprise applications, data pipelines, and IT infrastructure.
• Monitor solution performance (accuracy, latency, adoption) and drive continuous tuning and retraining of models.
• Guide business end-users during CRP, SIT, and UAT cycles to validate AI/automation solutions before go-live.
• Provide continuous post go-live support for issues, changes, and enhancements to deployed AI/automation solutions.

People Management:

• Mentor and support data scientists, AI/automation engineers, and citizen developers to work independently.
• Work collaboratively with a team encompassing many different backgrounds and experience levels.
• Attend training, conferences, and vendor briefings to stay abreast of the latest AI/agentic automation features and tools, and share learnings with the wider team.
• Motivate team members to maintain excellence in delivery and support opportunities for professional development.
• Manage vendor and consultant relationships for specialized AI/automation engagements, ensuring quality and knowledge transfer.
• Excellent interpersonal (verbal and written) communication skills are required to support working in project environments that include internal, external, and customer teams.

Product / Process Improvement:

• Own the roadmap for internally built 'products' (e.g., internal chatbots, decision-support tools, agentic workflow platforms), gathering user feedback and iterating for adoption and impact.
• Establish reusable frameworks, templates, and accelerators (prompt libraries, RAG pipelines, model deployment pipelines) to speed up future AI initiatives.
• Track and report the business impact/KPIs of deployed AI and automation solutions (efficiency gains, cost savings, accuracy improvements, revenue impact) to leadership.
• Drive a culture of experimentation (rapid prototyping, A/B testing) balanced with production-grade rigor.
• Continuously identify workarounds and propose changes to business processes to close gaps and remove redundancies.
• Act as an internal evangelist for AI/agentic automation—running training sessions, workshops, and awareness programs for business and IT stakeholders.
• Document solutions, playbooks, and lessons learned to build institutional knowledge and ensure sustainability beyond initial deployment.
• Support onboarding and upskilling of new team members on AI/automation tools, frameworks, and best practices.

Qualifications
Education:
Minimum:

• Bachelor's or master's degree in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field.
• End-to-end experience delivering AI/ML or automation solutions.
  Preferred:
• Relevant certifications in AI/ML, cloud platforms (Azure / AWS / GCP), or enterprise automation platforms (e.g., Microsoft Power Platform, UiPath) are an advantage.

Experience:

• 6-8+ years of progressive experience in data science, AI/ML engineering, or enterprise automation roles, including at least 2-3 years focused on generative AI/agentic AI solutions in a production environment.
• Proven track record designing, developing, and deploying end-to-end ML/AI solutions across multiple business domains, including predictive models, classification systems, recommendation engines, churn prediction models, pricing optimization frameworks, and intelligent chatbot systems.
• Demonstrated business impact through advanced analytics and AI-driven automation—for example, improving product grouping/classification accuracy, optimizing pricing strategies, reducing churn risk, and enabling data-driven decision-making.
• Experience working in a matrixed, multi-business-unit organization, ideally within a large group/conglomerate structure.

Knowledge & Skill:

• Strong programming skills in Python and PySpark; SQL; R.
• Machine learning & deep learning: XGBoost, logistic regression, Computer Vision, NLP, sentiment analysis, recommendation systems.
• ML/DL frameworks: Scikit-learn, TensorFlow, PyTorch; embeddings and transformer-based models for NLP and LLM applications.
• Generative AI & agentic AI: prompt engineering, RAG architectures, LLM orchestration frameworks (LangChain, Semantic Kernel, or similar).
• End-to-end ML lifecycle ownership: data preprocessing, feature engineering, model training, hyperparameter tuning, model evaluation, and production deployment.
• Big data & scalable data workflows: distributed processing (PySpark), handling structured and unstructured data at scale.
• MLOps: MLflow, Databricks, model monitoring, and retraining pipelines.
• Familiarity with enterprise application integration (APIs, ERP/CRM data models) and automation platforms (Power Automate, UiPath, or similar).
• Cloud platforms: Azure (preferred, given Microsoft ecosystem), AWS, or GCP.
• Strong stakeholder management and ability to translate technical solutions into business value for non-technical audiences.
• Analytical, structured problem-solving mindset with a bias for measurable impact.