Data Analytics Engineer

Requisition Number: 75978

The company built on breakthroughs. ​  
Join us.​    

Corning is one of the world’s leading innovators in glass, ceramic, and materials science. From the depths of the ocean to the farthest reaches of space, our technologies push the boundaries of what’s possible.  ​  

How do we do this? With our people. They break through limitations and expectations – not once in a career, but every day. They help move our company, and the world, forward. ​  

​At Corning, there are endless possibilities for making an impact. You can help connect the unconnected, drive the future of automobiles, transform at-home entertainment, and ensure the delivery of lifesaving medicines. And so much more.​   

Come break through with us.  

Corning's Manufacturing, Technology and Engineering division (MTE) is recognized as the leader in engineering excellence & innovative manufacturing technologies by providing diverse skills to Corning’s existing & emerging businesses.

We anticipate & provide timely, valued, leading edge manufacturing technologies and engineering expertise.  We partner with Corning’s businesses and the Science & Technology division. Together we create and sustain Corning’s manufacturing as a differential advantage.

Purpose of the Position:

As a member of the AI Agent & Knowledge Intelligence team, you will design and implement machine learning and AI algorithms that power production-ready AI Agent applications. You will own end-to-end delivery of key capabilities such as Retrieval-Augmented Generation (RAG), GraphRAG/knowledge graph retrieval, and evaluation/optimization of retrieval and generation quality, working closely with engineering and product partners to ship measurable impact.

Day to Day Responsibilities:

• Design and build LLM applications for process & manufacturing scenarios, such as shopfloor copilots for SOP/work-instruction Q&A, process parameter guidance, and engineering knowledge search (RAG/GraphRAG-enabled).
• Develop LLM-driven quality & troubleshooting workflows: summarize defect/abnormal logs, assist root-cause analysis (5-Why, fishbone), suggest containment actions, and continuously improve responses with evaluation metrics and feedback loops.
• Curate manufacturing knowledge assets: structure process routes, materials/BOM, equipment, defect codes, control plans, and change records; build/maintain knowledge graph where helpful to improve retrieval, traceability, and reasoning.
• Build AI Agent capabilities for manufacturing operations: tool/function calling to query MES/SCADA/ERP/QMS data, generate reports and tickets, and automate routine engineering workflows with proper permissions and guardrails.
• Enable equipment inspection & predictive maintenance use cases: interpret equipment alarms/sensor summaries, assist fault diagnosis, recommend inspection checklists and corrective actions, and help generate/standardize maintenance records with RAG-backed knowledge.
• Own LLM app quality in production with a focus on prompt engineering and retrieval strategy optimization: data cleaning, chunking, embedding/model selection, indexing, query rewriting, retrieval & re-ranking, grounding/citation, and offline/online evaluation (accuracy, latency) with monitoring and continuous iteration.
• Partner with process/manufacturing/quality engineers and IT teams to define use cases, validate outputs on real lines, document best practices, and drive adoption through pilots and iteration.

Education & Experience  

• Master’s degree or above in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.
• Solid foundation in Machine Learning and AI algorithms; candidates with demonstrable project experience (industry, research, competitions, or open-source) are preferred.
• Able to independently drive projects from problem definition to delivery, including technical design, implementation, evaluation, and iteration.

Required Skills                                                

• Proficient in Python and common ML tooling; able to implement and iterate algorithms with good engineering practices (clean code, logging, error handling, testing mindset).
• Hands-on knowledge of RAG systems and retrieval optimization (data ingestion/cleaning, chunking, embeddings, indexing, retrieval & re-ranking, grounding/citation); able to independently deliver an end-to-end RAG project.
• Understand AI Agent concepts and implementation (planning/orchestration, tool use/function calling, memory/state management); able to integrate RAG as a core agent capability.
• Knowledge graph fundamentals (entity/relation/schema) and GraphRAG patterns; able to model, store, query, and use a knowledge graph to improve retrieval and answer quality.
• Solid understanding of core ML/AI algorithms (e.g., classification/regression, representation learning, ranking) and evaluation; can define metrics and build evaluation sets (e.g., Recall@k, MRR, accuracy/F1) to drive iteration.
• Strong learning ability and independent thinking; excellent teamwork and communication skills, able to align with cross-functional stakeholders and drive execution.

Desired Skills  

• Proven experience delivering AI Agent, RAG/GraphRAG, or knowledge-graph-based systems in real projects (enterprise/search/QA/recommendation/operations automation).
• Experience with vector databases / retrieval stacks (e.g., FAISS, Milvus, pgvector, Elasticsearch) and performance/quality tuning.
• Experience with LLM/RAG evaluation (factuality/faithfulness, automated & human eval, A/B testing) and continuous improvement loops.
• Backend/API development experience (FastAPI/Flask) and basic deployment concepts (Docker) are a plus.
• Hardworking and accountable: strong ownership, quality-focused, and able to drive tasks to closure.
• Fast learner who can quickly understand context, break down problems, and iterate with discipline.
• Good collaboration habits: proactive status updates, risk transparency, and efficient teamwork.
• Work-life friendly: standard working hours, no overtime requirement; no business travel required.
• Frontier focus: work hands-on with RAG/GraphRAG across knowledge engineering, retrieval, generation, and evaluation.
• Real-world impact: build demos and components that can be reused and shipped into real internal products.
• Strong mentorship: guidance from a tech lead, code reviews, and a clear path to grow engineering depth.