Location:
Navi Mumbai, MH, IN, 400708
Requisition ID: 17702
About Holcim
As the world's global leader in innovative and sustainable building materials, Holcim is reinventing the way the world builds. Supported by a 60,000-strong global team spread across 70 markets and four industry segments, we are committed to shaping a greener, smarter and healthier world. We at Holcim believe that logistics performance is managed best locally at the country level and supported by corporate logistics. We envision to create a digitized supply chain managing the group's logistics performance in real-time and it is our aim to establish a true cost to serve model with decisions based on data to support reducing cost and business growth by improving customer service and achieving zero harm.
About The RoleQualifications:
- BE / B. Tech in Computer Science, Engineering or relevant field
- Graduate degree in Data Science or other quantitative field is preferred
- Strong mathematics skills (e.g. statistics, algebra)
- Certification in Gen/Agentic AI solutions
- Certification in Platforms – Databricks,AWS is preferred
Experience:
- 8+ years of progressive experience in data science and machine learning, with a minimum of 3 years focused on Generative AI and LLM-based systems.
- Demonstrated track record of delivering AI solutions at enterprise scale
- Hands-on experience with full AI/ML lifecycle management: data engineering, feature stores, model training, evaluation, deployment, and monitoring.
- Industry experience especially in Manufacturing Function in a Building Material Industry, Manufacturing, Process or Pharma is preferred.
Required skills:
- Proficiency in Python; strong working knowledge of relevant libraries & frameworks: LangChain, LangGraph, HuggingFace Transformers, PyTorch, scikit-learn, Pandas, and NumPy.
- Deep experience with LLM APIs (OpenAI, Anthropic, Google, Mistral) and open-source model deployment via Ollama, vLLM, or TGI.
- Solid command of vector databases, semantic search, and knowledge graph technologies for enterprise RAG architectures.
- Proficiency with MLOps tooling: MLflow, Weights & Biases, Kubeflow, or similar; experience with LLMOps tools such as LangSmith.
- Strong SQL and experience with modern data platforms such as Databricks for AI-ready data preparation.
- Understanding of software engineering best practices: version control (Git), containerization (Docker/Kubernetes), API design (REST/gRPC), and CI/CD pipelines.
- Knowledge of how to benchmark GenAI models beyond simple accuracy (e.g., toxicity, bias, and reasoning depth).
- Exposure to multi-modal AI systems incorporating vision, audio, or structured document understanding (PDFs, tables, charts).
- Good understanding of the GENAI standards (MCP, A2A, A2UI etc.)
Key Responsibility:
- Platform Prototyping: Design and implement core ML components, such as feature stores, model registries, and automated evaluation pipelines.
- Standardization: Establish best practices for the ML lifecycle, from data labeling and experimentation to CI/CD for ML (MLOps).
- Scalability: Optimize model inference and training workflows to handle high-throughput, low-latency requirements.
- Internal Consulting: Act as a subject matter expert for product-facing data science teams, helping them leverage platform tools to solve complex business problems.
- Tooling & Automation: Build internal libraries and SDKs that simplify the transition from a local research environment to a distributed production environment.
- RAG Infrastructure: Design and optimize high-performance retrieval systems using vector databases (e.g., Pinecone, Weaviate) and advanced semantic search techniques.
- LLM Evaluation Frameworks: Build automated vibe-check replacements. Develop rigorous evaluation pipelines using LLM-as-a-judge, G-Eval, or custom scoring rubrics to measure hallucination, faithfulness, and relevancy.
- Agentic Orchestration: Develop and standardize the use of agentic frameworks (e.g., LangGraph, CrewAI) to allow product teams to build complex, multi-step AI workflows.
- Model Lifecycle Management: Manage the transition between model providers (OpenAI, Anthropic, Google) and open-source alternatives (Llama 3+, Mistral) through unified abstraction layers.
- Cost & Latency Optimization: Implement caching strategies (e.g., GPTCache), prompt compression, and token-usage monitoring to ensure the platform remains economically viable.
- Guardrails & Safety: Integrate real-time content filtering and PII masking to ensure all LLM outputs comply with corporate security and ethical standards.
Result oriented and with a work ethic of delivering on-time and in scope
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