AI and ML Engineer

• Design, develop, and implement machine learning models and statistical analyses to address manufacturing, supply chain, and product quality challenges.
• Conduct exploratory data analysis and present actionable insights and recommendations to stakeholders.
• Develop prompt engineering strategies and feedback loops for LLM optimization; capture and normalize LLM interactions into reusable Knowledge Artifacts.
• Implement Retrieval-Augmented Generation (RAG) pipelines, including document parsing, chunking, vectorization, and semantic search using embeddings and vector databases. Support domain-specific ontologies, taxonomies, governance and versioning.
• Support agentic workflows using industry-standard frameworks for autonomous task orchestration and multi-step reasoning, with safe and controlled execution via constraints, policies, and fallback paths.
• Contribute to establishing and maintaining data quality standards, implementing validation frameworks and monitoring systems.
• Support the deployment and monitoring of machine learning models in production environments, including performance tracking and drift detection.
• Stay current with emerging data science technologies, methodologies, and industry trends, evaluating their applicability to organizational needs.
• Support end-to-end project delivery, ensuring timely completion and alignment with business objectives.