Data Scientist

Role Overview: You will be part of Holcim, the global leader in innovative and sustainable building materials, committed to shaping a greener, smarter, and healthier world. As a Data Scientist with a focus on Generative AI and LLM-based systems, you will play a crucial role in driving AI solutions at an enterprise scale in the Manufacturing Function of the Building Material Industry. Your expertise in data science, machine learning, and AI technologies will contribute to reducing carbon emissions and accelerating the transition towards low-carbon constructions globally. Key Responsibility: - Platform Prototyping: Design and implement core ML components like feature stores, model registries, and automated evaluation pipelines. - Standardization: Establish best practices for the ML lifecycle, from data labeling to CI/CD for ML (MLOps). - Scalability: Optimize model inference and training workflows for high-throughput, low-latency requirements. - Internal Consulting: Act as a subject matter expert for product-facing data science teams to solve complex business problems using platform tools. - Tooling & Automation: Build internal libraries and SDKs to simplify transitioning from a research to production environment. - RAG Infrastructure: Design high-performance retrieval systems using vector databases and semantic search techniques. - LLM Evaluation Frameworks: Develop evaluation pipelines to measure hallucination, faithfulness, and relevancy of AI models. - Agentic Orchestration: Standardize the use of agentic frameworks for complex, multi-step AI workflows. - Model Lifecycle Management: Manage transitions between model providers and open-source alternatives through unified abstraction layers. - Cost & Latency Optimization: Implement caching strategies, prompt compression, and token-usage monitoring for economic viability. - Guardrails & Safety: Integrate real-time content filtering and PII masking to ensure compliance with security and ethical standards. Qualification Required: - BE / B. Tech in Computer Science, Engineering, or relevant field. - Graduate degree in Data Science or other quantitative field preferred. - Strong mathematics skills (statistics, algebra). - Certification in Gen/Agentic AI solutions. - Certification in Platforms like Databricks, AWS preferred. - 8+ years of experience in data science and machine learning, with a focus on Generative AI and LLM-based systems. - Hands-on experience with full AI/ML lifecycle management. - Industry experience in Manufacturing Function in Building Material Industry, Manufacturing, Process, or Pharma preferred. - Proficiency in Python and relevant libraries/frameworks. - Experience with LLM APIs and open-source model deployment. - Knowledge of vector databases, semantic search, and knowledge graph technologies. - Proficiency with MLOps tooling and modern data platforms. - Understanding of software engineering best practices. - Exposure to multi-modal AI systems. - Good understanding of GENAI standards. Role Overview: You will be part of Holcim, the global leader in innovative and sustainable building materials, committed to shaping a greener, smarter, and healthier world. As a Data Scientist with a focus on Generative AI and LLM-based systems, you will play a crucial role in driving AI solutions at an enterprise scale in the Manufacturing Function of the Building Material Industry. Your expertise in data science, machine learning, and AI technologies will contribute to reducing carbon emissions and accelerating the transition towards low-carbon constructions globally. Key Responsibility: - Platform Prototyping: Design and implement core ML components like feature stores, model registries, and automated evaluation pipelines. - Standardization: Establish best practices for the ML lifecycle, from data labeling to CI/CD for ML (MLOps). - Scalability: Optimize model inference and training workflows for high-throughput, low-latency requirements. - Internal Consulting: Act as a subject matter expert for product-facing data science teams to solve complex business problems using platform tools. - Tooling & Automation: Build internal libraries and SDKs to simplify transitioning from a research to production environment. - RAG Infrastructure: Design high-performance retrieval systems using vector databases and semantic search techniques. - LLM Evaluation Frameworks: Develop evaluation pipelines to measure hallucination, faithfulness, and relevancy of AI models. - Agentic Orchestration: Standardize the use of agentic frameworks for complex, multi-step AI workflows. - Model Lifecycle Management: Manage transitions between model providers and open-source alternatives through unified abstraction layers. - Cost & Latency Optimization: Implement caching strategies, prompt compression, and token-usage monitoring for economic viability. - Guardrails & Safety: Integrate real-time content filtering and PII masking to ensure compliance with security and ethical standards. Q