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 physics 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 physicists with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:  - Degree in Physics or related fields, e.g. Engineering Physics, Thermodynamics, Statistical Mechanics, Optics and Acoustics, etc. - 3+ years of professional physics experience - Strong written English (C1/C2) - Strong Python proficiency for numerical validation - Stable internet connection  Professional certifications (e.g., CPhys, EurPhys, MInstP) 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.

• End-to-end ML pipelines: from feature engineering to deployment • RAG systems with semantic search and knowledge graphs • Scalable generative AI services in production environments • Robust data pipelines for high-volume workloads

• Design agent behaviours, reasoning flows, interaction patterns, delegation rules, and tool-use strategies • Develop orchestration logic for multi-step, multi-agent, and tool-augmented workflows • Create and refine prompts, system instructions, behavioural frameworks, validation rules, and safety guidelines • Build lightweight prototypes and POCs to validate agent logic and orchestration patterns • Run structured evaluations (reasoning consistency, tool-use correctness, stability) • Identify hallucinations, edge cases, and failure patterns; propose improvements • Collaborate with engineers to validate feasibility and maintainability • Work with architects, engineers, and business teams

• Define architectures for AI solutions (agents, orchestration, RAG/CAG, memory). • Produce architectural diagrams and define non-functional requirements. • Select appropriate design patterns. • Validate designs via reference artifacts or spikes when needed. • Design integrations between AI services and enterprise systems. • Define APIs, contracts, error handling, and authorization models. • Ensure interoperability across systems and platforms. • Provide reusable architecture patterns. • Define retrieval strategies (chunking, embeddings, vector stores). • Promote reusable components and templates. • Define acceptance criteria (accuracy, latency, cost, safety). • Establish evaluation scenarios and quality gates. • Ensure feedback and telemetry loops are included. • Embed security-by-design principles. • Address risks like prompt injection, data leakage, and misuse. • Ensure observability, traceability, and rollback strategies. • Define mitigation strategies for AI threats. • Align solutions with enterprise platforms (Azure OpenAI, etc.). • Ensure compliance with constraints and standards. • Identify gaps and propose solutions. • Make build vs. buy decisions. • Advise stakeholders on feasibility and trade-offs. • Lead design reviews and maintain architectural integrity. • Communicate decisions clearly. • Maintain architecture documentation and ADRs. • Define best practices and reusable standards.