Job Title : Sr Manager, Business Solutions, Data & AI

Key Responsibilities :

As the Sr Manager, Business Solutions, Data & AI, this role is responsible for leading the Data & AI charter for Kohler South Asia, driving strategic initiatives across the organization. A key focus is to embed AI capabilities within each business function to enhance productivity and operational effectiveness. Additionally, the position ensures that the data architecture remains robust and of high quality, recognizing its critical importance as the backbone of AI and other digital products.

1. ‘Data & AI’ Strategy & Create Demand

• Visionary Leadership: Define and evolve the Kohler South Asia-specific Data & AI roadmap, aligning it with global business objectives. Bring in ‘Outside In’ view and should be aware of what’s happening in the world of AI & Data. This role needs to be a Data & AI evangelist for the company
• Use Case Identification: Partner closely with business unit leaders to understand the As Is process end to end and identify "low-hanging fruit" and "moonshot" opportunities where Data & AI can drive revenue or operational efficiency.
• Value Quantification: Define KPIs and ROI frameworks to monitor the success of AI initiatives from inception to review.
• Business Solution Design: Brainstorm on & analyze the requirements with stakeholders and expose them to the Art of possible with Data & AI

2. Business Partnership & Create the ‘Data & AI’ Culture

• Culture of AI: Create a culture of AI to ensure each associate at Kohler South Asia uses AI to improve his/her productivity; Develop a change management strategy for the organization using gamifications etc
• Ensure Adoption of Copilot & other AI uses cases along with Data & Analytics Dashboards

3. Technical Architecture & System Integration

• Data Foundations: Designs, defines, and documents enterprise data architecture, creating ER diagrams, standards, and models to ensure scalable, governed, high-quality data solutions. Evaluate and optimize existing data architectures to ensure they support scalable AI models.
• Data Structure & Governance: Own the Data Structure for the region and ensure quality of data i.e completeness, accuracy. Own the Governance & Master Data Management for Kohler South Asia
• Tech Stack Selection: Select the right LLMs, frameworks, and infrastructure (Cloud/On-prem) tailored to specific business needs.
• Legacy Integration: Deep-dive into existing systems to ensure seamless data flow and integration between new AI agents and core business platforms.

4. Execution & Stakeholder and Vendor Management

• Orchestration: You will act as the "Technical Product Owner," translating business requirements into technical specs for external development teams.
• Vendor Selection: Lead the RFP process, evaluate vendor technical capabilities, and manage the delivery lifecycle.
• Product Delivery: Work with the Regional Product team & global AI CoE to deliver capabilities & use cases
• Quality & Review: Conduct rigorous code reviews, architecture audits, and performance monitoring to ensure vendor output meets global standards.
• Proof of Concept: Passionate about building your agents and playing around with different LLMs; collaborate with Architecture Office & AICoE.

Required Qualifications

• Technical Mastery: 12+ years of experience in IT/Software, with at least 5 years dedicated to AI/ML and Data Science and at least 5 years in Business Partnering roles.
• Architecture Depth: Strong expertise in Data Architecture, Data Lakes, and real-time processing systems. You should be able to "speak the language" of data engineers and developers fluently.
• Vendor Management: Experience managing high-end technology partners, ensuring they deliver quality on time.
• Education: B.Tech/M.Tech in Computer Science, Data Science, or a related field. An MBA is an added advantage

Technical Skills Required

1. Artificial Intelligence & Machine Learning

Core ML Fundamentals

• Supervised & Unsupervised Learning
• Deep Learning architectures (CNNs, RNNs, Transformers)
• NLP & Computer Vision fundamentals
• Time-series forecasting
• Recommendation systems

GenAI & LLM Expertise

• LLM evaluation and selection (open-source vs enterprise-grade)
• Prompt engineering frameworks
• Fine-tuning vs RAG-based architectures
• Embeddings and vector search mechanisms
• Multi-agent orchestration
• Guardrails, hallucination mitigation techniques

2. Data Architecture & Engineering

Enterprise Data Architecture

• Data Lake / Lakehouse design patterns
• Data Warehouse architecture
• Data Mesh / Domain-driven data concepts
• Master Data Management (MDM)
• Metadata management

Data Engineering

• ETL / ELT pipeline design
• Real-time streaming (Kafka or similar)
• API-based integration patterns
• Data quality frameworks
• Data governance controls

Storage & Processing

• Structured & unstructured data handling
• Vector databases (for RAG architectures)
• Distributed processing frameworks (Spark, Databricks, etc.)

3. Understanding of Cloud & Infrastructure

• Azure / AWS / GCP architecture design (Azure desired)
• AI-native services (Azure OpenAI, SageMaker, Vertex AI, etc.)
• CI/CD pipelines for ML (MLOps)

4. Security, Governance & Responsible AI

• AI governance frameworks
• Data privacy regulations awareness
• Role-based access controls
• Secure model deployment
• Ethical AI and bias mitigation principles
• Audit logging and traceability mechanisms

5. Architecture & Design Skills

• Solution architecture documentation
• Technical blueprint creation
• Scalability & performance design
• High-availability system design
• Build vs Buy evaluation frameworks
• Cost optimization modeling (cloud & AI workloads)

6. Technical Evaluation & Vendor Oversight

• Code review capability
• Architecture review capability
• RFP technical evaluation
• SLA and performance governance
• Benchmarking and proof-of-value validation