Please submit your resume 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 isproject-based, not permanent employment. What this opportunity involves As an AI Trainer - Junior Linguist, your work will help train AI models, shaping how they understand and generate human-like text. This isn’t just traditional writing—you’ll be crafting text that teaches AI reasoning, logic accuracy, nuance, and clarity. While each project involves unique tasks, contributors may: - Craft original, clear, and fact-checked responses based on project guidelines. - Generate prompts that challenge AI. - Define comprehensive scoring criteria to evaluate the accuracy of the AI’s answers. - Follow style and quality standards to ensure consistency. What we look for This opportunity is a good fit if you are seeking for open to part-time, non-permanent projects. Ideally, contributors will have:  - Bachelor’s degree in Linguistics, Computational Linguistics, Translation Studies, Literature, or any related education to ensure a strong understanding of grammar and stylistic features; - At least 1 year of professional experience in Linguistics, Research in Computational Linguistics, Translation Projects, etc., with strong skills in critical thinking and working with text in English language; - Strong written English (C1/C2); - Stable internet connection.  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 $30 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.

Role Description We are looking for a talented and innovative Large Language Model (LLM) Engineer with specialized experience in Retrieval Augmented Generation (RAG) to join our dynamic team. The ideal candidate will be instrumental in the development, optimization, and deployment of RAG-based LLM solutions that meet various business needs. This role requires a deep understanding of Data Science, Machine Learning (ML), Natural Language Processing (NLP), and the fundamentals of LLMs. The successful candidate will have hands-on experience with agent-based prompting techniques such as LangChain, API-driven development architectures for AI products, usage of vector databases, and information retrieval pipelines to extract actionable insights. Additionally, strong listening skills and an ability to understand business requirements are essential for designing effective solutions tailored to stakeholder needs. Job Responsibilities - Designing RAG Solutions: Develop robust RAG-based LLM solutions that enhance data retrieval processes while ensuring high quality output aligned with business objectives. - Prompt Engineering: Craft effective prompts that efficiently guide LLMs towards generating outputs that fulfill specific business requirements; iterate on prompt designs based on performance metrics. - AI Product Development: Design, develop, and refine AI products based on user feedback and stakeholder requirements; ensure alignment with program priorities through agile methodologies. - Support ML Engineering Teams: Collaborate closely with ML engineering teams during the deployment phase of APIs in production environments; assist in troubleshooting issues related to model performance or integration challenges. - Documentation Creation: Document Generative AI use cases comprehensively; create manuals detailing workflows, methodologies employed in solution design, and best practices adopted throughout the process. - Retrieval Augmented Generation Maintenance: Develop and maintain RAG concepts following established data science principles; ensure adherence to industry standards while fostering innovation. - Pipeline Optimization: Ensure efficient operation of data pipelines and processes according to company security policies; promote Responsible AI practices within all project phases. - Collaboration & Communication: Work closely with cross-functional teams including product managers, data scientists, software engineers, and stakeholders to align goals effectively; communicate technical concepts clearly across diverse audiences. Qualifications - A minimum of 3 years' experience working with Python for machine learning applications along with expertise in AWS Cloud Computing services. - Familiarity with AWS Bedrock's suite of LLM models along with associated prompting strategies tailored for optimal performance. - At least 1 year of direct experience working specifically within NLP frameworks/LLMs focusing on Retrieval Augmented Generation models. - Proficiency in Python programming language utilized extensively for data science projects involving AI development. - Familiarity with agent-based frameworks like LangChain which support advanced interactions between users/models. - Experience leveraging AI training frameworks such as TensorFlow or PyTorch alongside Hugging Face Transformers library for model building/training tasks. - Demonstrated expertise in prompt engineering techniques aimed at fine-tuning large language models tailored toward specific applications or industries. - Knowledgeable about vector databases including practical experience implementing embedding techniques necessary for enhanced information retrieval capabilities. Preferred Skills - Experience utilizing orchestration tools designed specifically for AI product development (e.g., ArgoCD). - Ability to create detailed diagrams illustrating data flows/solutions which can effectively communicate complex ideas/processes among team members/internal stakeholders alike. - Prior experience deploying UI/frontend components related directly back into existing large language model systems would be advantageous.

• Lead the design and productionization of machine learning, ML ops, traditional AI, LLMs, and agentic AI solutions into scalable, product-driven applications, serving as a technical expert. • Drive the implementation of complex application integrations to leverage and deploy machine learning, ML ops, traditional AI, LLMs, and agentic AI products. • Define and implement architectural solutions that ensure the delivery speed and scalability of product and data pipelines. • Lead the adoption of managed and serverless cloud offerings to create performant and scalable cloud-native application solutions and data pipelines. • Lead the implementation of advanced machine learning models including regression, classification, Bayesian methods, Retrieval-Augmented Generation (RAG), and advanced Agentic AI architectures. • Serve as a technical leader to integrate prompt engineering and RAG components into production systems, ensuring seamless and robust deployment. • Serve as a key resource in client-facing consulting, driving customer engagement and influencing technical solutions for clients. • Stay up to date with the latest advancements in prompt engineering, RAG, data science, machine learning, and AI technologies, exploring innovative approaches to enhance machine learning, ML ops, traditional AI, LLMs, and agentic AI capabilities across the organization.

Role Description We are building an AI-native company where core business processes — product development, content production, and growth — are executed by systems of AI agents. As an AI Agents Engineer, you will design and build these systems end-to-end, working at the intersection of product, engineering, and AI. - Design multi-agent systems to automate business workflows (content, product, growth). - Build and integrate AI agents using LLMs, APIs, and internal tools. - Define workflows, input/output contracts, and system architecture. - Continuously improve agent performance based on real metrics and feedback loops. Qualifications - Strong engineering background (3-6 years as a Backend or Full-stack engineer). - Experience building real products or systems with high ownership. - Hands-on experience working with LLMs and AI tools in practical use cases (not only theoretical knowledge). - Ability to design and implement end-to-end workflows, including defining inputs, outputs, and system logic. - Experience integrating multiple external services and APIs (e.g., Notion, analytics tools, CRM, CI/CD). - Strong problem-solving and systems thinking skills — ability to break down complex problems into structured solutions. - Comfort working in ambiguous environments without fully defined requirements. - Ability to quickly prototype and iterate on solutions, balancing speed and quality. - Strong communication skills and ability to collaborate with non-technical stakeholders. Benefits - Remote-first work format with access to Genesis offices in Kyiv and Warsaw. - Flexible schedule — most of the team works roughly 9:00 to 18:00 for convenient communication. - Learning and growth opportunities: access to an educational hub, professional communities, workshops, and specialized courses. - Company-provided equipment when needed. - Support for private entrepreneurs (Ukraine/Poland): assistance with tax reporting. - Corporate doctor available in Kyiv and Lviv. - Medical insurance (USG) for employees in Ukraine after the probation period. - 20 paid working days of vacation (4 weeks / 28 calendar days). - Corporate discounts on sports, education, beauty, health, clothing, cleaning, entertainment, and gifts.