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 isproject-based, not permanent employment. What this opportunity involves While each project involves unique tasks, contributors may: - Design graduate- and industry-level mechanical engineering problems grounded in real practice. - Evaluate AI-generated solutions for correctness, assumptions, and engineering logic. - Validate analytical or numerical results using Python (NumPy, SciPy, Pandas). - Improve AI reasoning to align with first principles and accepted engineering standards. - Apply structured scoring criteria to assess multi-step problem solving. What we look for This opportunity is a good fit for mechanical engineers with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:  - Degree in Mechanical Engineering or related fields, e.g. Thermodynamics, Fluid Mechanics, Mechanical Design, Computational Mechanics, etc. - 3+ years of professional mechanical engineering experience - Strong written English (C1/C2) - Strong Python proficiency for numerical validation - Stable internet connection  Professional certifications (e.g., PE, CEng, PMP) 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. Payment - Paid contributions, with rates up to $55/hour*  - Fixed project rate or individual rates, depending on the project - Some projects include incentive payments *Note: Rates vary based on expertise, skills assessment, location, project needs, and other factors. Higher rates may be offered to highly specialized experts. Lower rates may apply during onboarding or non-core project phases. Payment details are shared per project.

• Assist in building the "connectors" between LLMs and real-world data tools (Airflow, dbt, Microsoft Fabric). • Help develop validation layers to ensure AI-generated code (SQL/Python) is syntactically correct and follows governance standards. • Support the implementation of multi-step reasoning cycles (Plan-Act-Observe) using frameworks like LangGraph or CrewAI. • Work with Azure Functions and Container Apps to deploy and scale AI-driven microservices. • Build automated test suites to benchmark agent performance and detect regressions in reasoning.

Position Summary Vista Equity Partners is seeking a hands-on Enterprise AI Platform Architect to drive AI innovation across our 90+ portfolio companies. This role sits at the center of Vista’s Agentic Factory, our methodology for collaborating with portfolio companies to identify high-value agentic use cases and rapidly build production AI agents that deliver measurable business impact. The ideal candidate combines deep technical expertise in generative AI, agents, and agentic architectures with the ability to move fast, shipping MVPs in compressed timelines and designing platforms that perform reliably in enterprise environments. You will work directly with portfolio company technology teams to design, implement, and scale AI solutions that enhance product offerings and drive competitive advantage. This is a deep technical role regarding hands-on work alongside the portfolio engineering teams. Responsibilities Portfolio Company Collaboration & Agentic Delivery - Partner with portfolio company technology and product teams to identify and prioritize agentic AI use cases aligned with their product roadmaps and business objectives. - Lead end-to-end delivery of AI agent implementations using Vista’s Agentic Factory methodology, targeting MVP delivery in under a quarter. - Run rapid proof-of-value sprints that demonstrate measurable business impact, then guide portfolio companies from prototype through production deployment. - Lead AI maturity assessments across the portfolio using Vista’s scoring rubrics, identifying gaps and creating tailored improvement roadmaps. - Conduct architectural reviews and provide recommendations for optimizing AI platform performance, scalability, and security. AI Platform Architecture & Design - Design scalable, resilient, and secure AI platforms leveraging public cloud AI services (AWS, Azure, GCP) while maintaining vendor independence through thoughtful abstraction layers. - Develop and maintain reference architectures for AI applications, including generative AI models, agentic systems, and multi-agent collaboration patterns. - Design appropriate autonomy levels for enterprise agent deployments, incorporating human-in-the-loop checkpoints calibrated to risk, regulatory requirements, and domain complexity. - Define standards for AI model development, deployment, monitoring, and governance, including data privacy, model provenance, and audit trails for agent decision-making. AI Innovation & Strategy - Serve as a subject matter expert on generative AI, agents, and agentic architectures, providing thought leadership and strategic guidance to portfolio companies and Vista leadership. - Identify and evaluate emerging AI technologies and trends, assessing their potential impact on portfolio company product roadmaps and Vista’s investment thesis. - Develop and evangelize best practices for AI platform design, development, and deployment across the portfolio. Due Diligence - Assist in the due diligence process for potential portfolio companies, assessing AI technology capabilities, technical debt, and readiness for agentic AI adoption. - Provide technical assessments that inform investment decisions and post-acquisition value creation plans. Technical Expertise - Generative AI including LLMs, diffusion models, and RAG architectures - Agentic architectures, autonomous agents, and multi-agent systems - Cloud AI platforms across AWS, Azure, and GCP - AI model development, deployment, and lifecycle management - Prompt engineering and model fine-tuning - Machine learning and deep learning fundamentals - Platform abstraction and vendor-independent architecture design Operational Expertise - Rapid proof-of-value delivery and compressed development timelines - Cross-organizational collaboration and stakeholder management across multiple companies simultaneously - AI maturity assessment and improvement planning - Translating complex technical concepts for executive audiences - Problem-solving and analytical thinking in ambiguous environments Qualifications - Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field. - 3+ years ofexperience in AI/ML platform architecture and development, with deep recent experience (2+ years) in generative AI and agentic architectures in production applications. - Demonstrated track record of shipping AI applications to production environments, not just prototypes. - Strong understanding of public cloud AI services and the ability to architect vendor-agnostic solutions with appropriate abstraction layers. - Excellent communication, presentation, and interpersonal skills. - Experience within private equity portfolio companies, consulting, or multi-client environments is a strong plus. The annualized base pay range for this role is expected to be between $200,000 - $315,000. Actual base pay could vary based on factors including but not limited to experience, subject matter expertise, geographic location where work will be performed and the applicant’s skill set. The base pay is just one component of the total compensation package for employees. Other components may include an annual cash bonus and a comprehensive benefits package.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description Our client is seeking a Full-Stack AI Engineer to design, build, and deploy AI-powered applications. This role requires bridging software engineering with applied machine learning, ensuring that models are integrated into production systems that are scalable, reliable, and user-friendly. The Full-Stack AI Engineer combines back-end services, front-end interfaces, and machine learning pipelines to deliver practical, business-driven AI solutions. Responsibilities - AI Model Integration: - Deploy pre-trained and fine-tuned ML/LLM models (OpenAI, Hugging Face, TensorFlow, PyTorch). - Wrap models in APIs (FastAPI, Flask, Node.js) for scalable inference. - Implement vector search integrations (Pinecone, Weaviate, FAISS) for retrieval-augmented generation (RAG). - Data Engineering & Pipelines: - Build ETL pipelines for ingesting, cleaning, and transforming text, image, or structured data. - Automate data labeling, preprocessing, and versioning with Airflow, Prefect, or Dagster. - Store and manage datasets in cloud warehouses (Snowflake, BigQuery, Redshift). - Application Development (Full-Stack): - Build front-end UIs in React, Next.js, or Vue to surface AI-powered features (chatbots, dashboards, analytics). - Design back-end services and microservices to connect models to business logic. - Ensure responsive, intuitive, and secure interfaces for end users. - Infrastructure & Deployment: - Containerize ML services with Docker and deploy to Kubernetes clusters. - Automate CI/CD pipelines for model updates and application releases. - Monitor latency, cost, and model drift with MLflow, Weights & Biases, or custom dashboards. - Security & Compliance: - Ensure AI systems comply with data privacy standards (GDPR, HIPAA, SOC 2). - Implement rate limiting, access control, and secure API endpoints. - Collaboration & Iteration: - Work with data scientists to productionize prototypes. - Partner with product teams to scope AI features aligned with business needs. - Document systems for reproducibility and knowledge transfer. Qualifications - Strong coder with a foundation in both full-stack development and applied ML/AI. - Comfortable building prototypes and scaling them to production-grade systems. - Analytical problem solver who balances performance, cost, and usability. - Curious and adaptable, staying current with emerging AI/LLM tools and frameworks. Requirements - 3+ years in software engineering with exposure to AI/ML. - Proficiency in Python (PyTorch, TensorFlow) and JavaScript/TypeScript (React, Node.js). - Experience deploying ML models into production systems. - Strong SQL and experience with cloud data warehouses. Ideal Experience & Skills - Built and scaled AI-powered SaaS products. - Experience with LLM fine-tuning, embeddings, and RAG pipelines. - Knowledge of MLOps practices (Kubeflow, MLflow, Vertex AI, SageMaker). - Familiarity with microservices, serverless architectures, and cost-optimized inference. What Does a Typical Day Look Like? A Full-Stack AI Engineer’s day revolves around connecting models to real-world applications. You will: - Review and refine model APIs, testing latency and accuracy. - Write front-end code to surface AI features in user-friendly interfaces. - Maintain pipelines that clean and prepare new datasets for training or fine-tuning. - Deploy updates through CI/CD pipelines, monitoring cost and performance post-release. - Collaborate with product and data science teams to prioritize AI features that solve real user problems. - Document workflows and results so solutions are repeatable and scalable. Key Metrics for Success (KPIs) - Successful deployment of AI features to production on schedule. - Application uptime ≥ 99.9% and inference latency < 500ms for key endpoints. - Reduction in manual workflows replaced by AI features. - Model performance tracked and stable (accuracy, drift, false positives/negatives). - Positive user adoption and satisfaction of AI-driven features. Interview Process - Initial Phone Screen - Video Interview with Pavago Recruiter - Technical Assessment (e.g., deploy a small ML model with API endpoints and basic front-end integration) - Client Interview(s) with Engineering Team - Offer & Background Verification