Senior Data Solutions Engineer

Summary

Join Datacor, a leading provider of software solutions for process manufacturing, and help us build innovative AI applications. Collaborate with stakeholders to identify new AI opportunities and lead MVP development using a test-and-learn approach. Iterate from problem framing to deploying production-grade solutions, delivering reliable and scalable AI features. You will build prototypes, develop data pipelines, and experiment with generative LLMs. Effectively communicate your work to both technical and business audiences. This role requires a Master's or PhD in a related field and proven expertise in implementing and fine-tuning generative AI LLMs.

Requirements

• Master’s or PhD degree in computational data science, computer science, electrical/computer engineering, or related fields, plus 2 years of relevant experience
• Proven expertise in implementing and fine-tuning generative AI LLMs, with strong NLP knowledge including embeddings, GPT, and Transformer models
• Familiarity with cloud computing and AI deployment on AWS or Google Cloud
• Solid foundation in deep learning, machine learning, statistics, and optimization from coursework or practical experience
• Proficient in Python & C++/Java programming through academic or work experience
• Expertise in the latest Large Language Models (LLMs) and AI advancements
• Proactive in staying updated with evolving AI trends and new LLM releases
• Skilled at diagnosing and solving complex, ambiguous problems with curiosity and a product-focused mindset
• Self-motivated, asking critical analytical questions to drive impactful product outcomes
• Effective collaborator across teams, including operations, product, and engineering
• Strong communication and presentation skills, able to convey complex analysis clearly and actionably
• Results-oriented, creative thinker who takes initiative and delivers practical solutions
• Committed to inclusivity, respecting diverse perspectives to foster innovation and build great products

Responsibilities

• Collaborate with stakeholders to identify new AI opportunities & create intelligent AI applications
• Lead MVP development using a test-and-learn approach with cross-functional teams
• Iterate from problem framing to prototyping and deploying production-grade solutions
• Deliver reliable, scalable AI features in production
• Build prototypes and develop data pipelines for deployment
• Experiment with generative LLMs to create and enhance generative applications using prompts and user feedback
• Clearly communicate efforts and impact to both technical and business audiences