Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment. What this opportunity involves While each project involves unique tasks, contributors may: - Design rigorous mathematics problems reflecting professional practice; - Evaluate AI solutions for correctness, assumptions, and constraints; - Validate calculations or simulations using Python (NumPy, Pandas, SciPy); - Improve AI reasoning to align with industry-standard logic; - Apply structured scoring criteria to multi-step problems. What we look for This opportunity is a good fit for mathematicians with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:  - Degree in Mathematics or related fields, e.g. Algebra, Calculus, Number theory, etc. - 3+ years of professional mathematics experience - Strong written English (C1/C2) - Strong Python proficiency for numerical validation - Stable internet connection  Professional certifications (e.g., CMME, SAS Certifications, CAP) and experience in international or applied projects are an advantage. How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. Compensation On this project, contributors can earn up to $55 per hour equivalent, depending on their level and pace of contribution. Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.

AI Agent Developer (LLM Specialist) Role Overview The AI Agent Developer is responsible for designing, building, and deploying intelligent autonomous or semi-autonomous agents powered by Large Language Models (LLMs). This role focuses on enabling complex workflows, reasoning capabilities, system integration, and secure data interaction across enterprise environments. Key Responsibilities 🔹 Agent Design & Development - Architect and implement AI-powered agents using LLMs (e.g., GPT, Claude, Llama, Mistral, Gemini). - Develop prompt workflows, retrieval-augmented generation (RAG), and multi-step reasoning. - Configure agent behaviors for decision-making, task execution, and contextual understanding. - Implement function calling, tool use, or API integration to extend agent capabilities. 🔹 System Integration - Connect agents to internal systems, databases, knowledge bases, and APIs. - Use frameworks such as LangChain, LlamaIndex, Haystack, Semantic Kernel, or custom pipelines. - Implement connectors for CRMs, ERPs, ticketing platforms, or document repositories. 🔹 Data & Knowledge Management - Create structured and unstructured knowledge retrieval mechanisms. - Build and manage vector databases (Pinecone, Weaviate, Chroma, Milvus, Azure AI Search). - Implement embeddings, chunking strategies, metadata tagging, and query optimization. 🔹 Workflow Automation - Design agent orchestration pipelines to handle multi-agent collaboration. - Enable agents to automate research, reporting, email drafting, document processing, and customer interactions. - Integrate with workflow tools like Zapier, Airflow, UiPath, or Power Automate. 🔹 Model Optimization & Evaluation - Fine-tune or customize LLMs using organization-specific data. - Evaluate model performance for accuracy, latency, cost, hallucinations, and compliance. - Apply guardrails, prompt validation, and reinforcement techniques. 🔹 Security, Ethics & Compliance - Enforce data privacy and governance standards (GDPR, HIPAA, SOC2, ISO, CCPA). - Implement role-based access and secure API token handling. - Apply content filtering, moderation, and usage monitoring. 🔹 Collaboration & Documentation - Work with cross-functional teams (engineering, design, product, legal, security). - Maintain technical documentation, agent configuration logs, and deployment playbooks. - Train internal teams on capabilities and responsible use.

• Adapt LLMs to diverse healthcare use-cases using techniques such as Sparse Fine-Tuning (SFT), Prompt Engineering Fine-Tuning (PEFT), Direct Parameter Optimization (DPO), and Proximal Policy Optimization (PPO). • Optimize LLMs for Retriever-Augmented Generation (RAG) to enhance decision-making and information retrieval capabilities. • Collect, clean, and refine healthcare datasets for training LLMs to ensure high-quality data provisioning. • Convert models into various formats suitable for production environments, ensuring their readiness for real-world application.

• Σχεδιασμός και ανάπτυξη λύσεων NLP με χρήση Large Language Models (π.χ. OpenAI, HuggingFace Transformers, LLaMA, Mistral κ.ά.). • Fine-tuning και βελτιστοποίηση προεκπαιδευμένων μοντέλων σε εξειδικευμένα datasets. • Ανάπτυξη pipelines για data preprocessing, training, evaluation και deployment σε cloud/edge περιβάλλοντα. • Συμμετοχή σε έργα R&D που ενσωματώνουν AI σε επιχειρησιακές και ερευνητικές πλατφόρμες. • Συνεργασία με Software Engineers, UI/UX Designers και Systems Architects για την ολοκληρωμένη υλοποίηση λύσεων. • Συμβολή στη συγγραφή τεχνικών παραδοτέων και τεκμηρίωσης AI modules. • Παρακολούθηση εξελίξεων στον χώρο του NLP & LLMs και εισαγωγή νέων μεθοδολογιών.