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Job Description
Structured overview of role & requirementsAbout This Role
Own the end-to-end design and implementation of the enterprise intelligence layer transforming process knowledge into a governed, provenance-rich ontology.
Develop and maintain extraction and learning systems that attach source evidence and epistemic status, ensuring evidence-backed outputs with zero hallucination.
Build and enhance frameworks for large-scale inference, model adaptation loops, RL environments, and evaluation metrics to deliver production-grade AI systems in regulated environments.
Minimum Requirements
PhD in Artificial Intelligence, Computer Science, or a related field.
At least 4 years of relevant industry experience in AI research with prototype-to-production delivery in regulated settings.
Deep expertise in Python, PyTorch, large-scale model fine-tuning techniques (SFT, DPO, RFT), and ontology engineering including graph databases, schema design, version control, and provenance tracking.
Work Experience Required: Minimum 4 years industry experience in research roles.
Ideal Candidate Profile
Experienced in operating at the intersection of AI research and regulated enterprise infrastructure delivery with track record of balancing applied research and engineering.
Skilled in designing complex knowledge-graph schemas and extraction pipelines with focus on traceable, evidence-backed AI outputs.
Able to lead design and execution of RL environments, curriculum design, and rigorous evaluation to optimize autonomous system safety and performance in production.