Senior Applied Scientist

AI Solution Development:

• Design, build, and deploy scalable machine learning and AI models, including NLP and LLM-based systems, translating complex business problems into production-ready solutions.
• Architect end-to-end AI systems, including data pipelines and deployment environments, ensuring scalability and reliability while integrating modern approaches like retrieval-augmented generation.
• Continuously refine models through experimentation and monitoring to improve accuracy, reduce latency, and proactively address drift and bias.

AI Engineering and MLOps:

• Build and maintain robust machine learning pipelines with automated testing and CI/CD integration, enabling rapid experimentation and deployment in alignment with enterprise standards.
• Implement rigorous evaluation frameworks and governance practices to ensure responsible AI usage, focusing on explainability and compliance within regulated environments.
• Develop and manage monitoring solutions that track model performance, detect drift, and trigger retraining workflows to ensure sustained reliability.

Cross-Functional Impact and Leadership:

• Collaborate with engineering, data, and business leaders to identify high-value AI opportunities, clearly communicate technical concepts, and influence decision-making aligned with strategic priorities.
• Translate complex business challenges into actionable AI solutions, balancing technical feasibility with business impact to drive innovation and efficiency across functional domains.
• Mentor peers and contribute to enterprise AI strategy by sharing best practices and promoting a culture of innovation, accountability, and continuous improvement.