Advanced AI Data Scientist – Large Language Model (LLM) Specialist for Customer Service Scheduling Optimization

```html Join Workora – Pioneering the Future of Digital Finance and AI‑Driven Customer Experience Hiretide stands at the forefront of the global blockchain and digital‑asset ecosystem, empowering millions of users across more than 100 countries with secure, lightning‑fast trading, deep liquidity, and an ever‑expanding suite of financial products. Our mission is to democratize finance, unlock the freedom of money, and create an inclusive ecosystem where anyone can participate in the digital economy. As part of our relentless push to innovate, Taskzeno is expanding its AI capabilities to transform how we serve customers. Our Customer Service Scheduling platform is a critical touchpoint that determines how quickly users receive help, how efficiently support teams operate, and ultimately, how satisfied our community feels. To stay ahead of the curve, we are building cutting‑edge Large Language Model (LLM) solutions that will automatically understand, prioritize, and route service requests, delivering a seamless, intelligent experience at scale. Why This Role Matters If you thrive on turning complex data into actionable intelligence, love engineering state‑of‑the‑art language models, and want to see your work directly impact millions of users, this is the opportunity for you. As a Data Scientist – LLM (Customer Service) at Giglithic, you will be the technical catalyst behind a next‑generation scheduling engine, shaping the future of AI‑enhanced support for a fast‑growing global fintech leader. Key Responsibilities Research & Implementation: Conduct deep research into algorithmic strategies for optimizing customer‑service scheduling, from reinforcement‑learning approaches to probabilistic forecasting. Domain Expertise: Leverage an in‑depth understanding of Talentra’s scheduling workflows, escalation hierarchies, and service‑level agreements (SLAs) to translate business needs into data‑driven solutions. LLM Development & Fine‑Tuning: Design, train, and continuously improve large language models that can interpret unstructured support tickets, extract intent, and propose optimal scheduling actions. Prompt Engineering: Craft, test, and iterate on prompt designs that guide LLMs to produce accurate, context‑aware outputs, reducing hallucinations and improving relevance. Scalable RAG Frameworks: Build Retrieval‑Augmented Generation (RAG) pipelines that combine LLM reasoning with real‑time knowledge bases, ensuring the model stays up‑to‑date with policy changes and new product releases. Performance Monitoring: Implement robust evaluation metrics (e.g., latency, precision/recall, SLA compliance) and dashboards to track model impact on scheduling efficiency. Collaboration & Communication: Partner with product managers, UI/UX designers, and engineering teams to integrate AI insights into user‑facing tools and back‑office dashboards. Innovation Advocacy: Stay abreast of emerging AI research, contribute to internal knowledge‑sharing sessions, and champion best practices for responsible AI deployment. Essential Qualifications Master’s degree or higher in Computer Science, Data Science, Statistics, Mathematics, Computational Linguistics, or a related quantitative discipline. At least 3 years of professional experience building, deploying, or researching AI/ML solutions, with a strong focus on large language models and generative AI. Proficiency in Python (core libraries such as NumPy, pandas) and familiarity with Java or Scala for production‑grade pipelines. Hands‑on expertise with deep‑learning frameworks (TensorFlow, PyTorch) and NLP toolkits (SpaCy, NLTK, Hugging Face Transformers). Demonstrated experience in prompt engineering, fine‑tuning LLMs, and constructing Retrieval‑Augmented Generation pipelines. Solid track record of applying machine‑learning techniques to real‑world business problems, preferably within customer‑service or contact‑center environments. Exceptional analytical mindset, capable of translating vague business problems into concrete, data‑driven experiments. Strong communication skills – ability to convey complex technical concepts to non‑technical stakeholders. Preferred Qualifications & Nice‑to‑Have Skills Ph.D. in a relevant field with published research on LLMs, reinforcement learning, or causal inference. Experience with cloud platforms (AWS, GCP, Azure) and MLOps tools (Kubeflow, MLflow, Terraform) for scalable model deployment. Familiarity with graph databases or knowledge‑graph technologies that can enhance RAG pipelines. Background in operations research or optimization (e.g., linear programming, integer programming) to complement scheduling algorithms. Knowledge of data privacy regulations (GDPR, CCPA) and best practices for responsible AI. Previous involvement in fintech, blockchain, or cryptocurrency ecosystems, providing contextual insight into Flexoraq’s market. Core Skills & Competencies Algorithmic Thinking: Ability to design and evaluate sophisticated AI models that balance accuracy, spee