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

• Serve as the most senior technical leader on major engagements, advising C-level executives and key stakeholders on their AI strategy, architectural roadmap, and governance model. • Recruit, mentor, and manage a team of Senior and Principal AI Engineers, fostering a culture of technical excellence, accountability, and continuous learning. • Own the technical success of the Professional Services portfolio, setting the technical standards, ensuring solution quality, and overseeing the deployment of advanced AI applications. • Define the architectural blueprint and MLOps best practices for building robust, scalable, and secure AI solutions that leverage the DataRobot platform. • Act as a recognized subject matter expert in the AI/ML community, representing DataRobot at industry conferences and driving internal innovation. • Drive successful program execution, managing technical risk and ensuring high customer satisfaction.

Job Title: AI Solutions Developer, Content Solutions Location: Remote Who We Are NWEA® is a division of Houghton Mifflin Harcourt that supports students and educators through research, assessment solutions, policy and advocacy services, professional learning and school improvement services that fight for equity, drive classroom impact and push for systemic change in our educational communities. For nearly 50 years, NWEA has developed innovative pre-K–12 assessments, including their flagship interim assessment, MAP® Growth™ and their reading fluency and comprehension assessment, MAP® Reading Fluency™. For more information, visit NWEA.org to learn more. What You’ll Do This position focuses on supporting AI development and integration through hands-on technical work, including coding, testing, and system monitoring. While primarily technical, the role also involves Agile coordination to keep tasks organized and ensure smooth communication across teams. Responsibilities - AI Development & Coding - Assist in building and testing AI applications using platforms like ChatGPT, Azure AI, or similar. - Write and maintain clean, efficient code in Python and/or JavaScript. - Support integration of AI features into products and services. - Develop scripts and tools to automate workflows and improve AI performance. - Quality Assurance & Performance - Execute test scripts, log bugs, and assist in debugging AI-related systems. - Monitor AI system performance and document issues for resolution. - Validate outputs for accuracy and reliability. - Documentation & Collaboration - Create and maintain technical documentation, user guides, and workflow diagrams. - Collaborate with developers, data scientists, and product teams to ensure alignment. - Agile Support - Work with Delivery Lead to update and maintain backlogs in Agile tools. - Track sprint progress and provide concise status updates. - Facilitate communication between technical and non-technical stakeholders. What You’ll Need - Bachelor’s degree in computer science, Information Technology, or related field; equivalent experience or projects acceptable. - Strong understanding of AI concepts, machine learning workflows, and model deployment basics. - Proficiency in Python and/or JavaScript. - Familiarity with AI platforms (ChatGPT, Azure AI, etc.). - Exposure to Agile methodology and backlog management tools. - Ability to write clear technical documentation, including system and workflow diagrams. - Strong attention to detail and QA skills. Preferred Qualifications - Certifications in AI, data science, or cloud technologies. - Experience with REST APIs, cloud-based development, and version control (Git). - Knowledge of data preprocessing and model evaluation techniques. - Hands-on projects or internships in AI development. - Familiarity with databases and integration workflows. Skills - Technical Problem-Solving: Ability to debug, optimize, and validate AI systems. - Coding Proficiency: Comfortable writing scripts and working with APIs. - Attention to Detail: Critical for QA and performance monitoring. - Adaptability: Ability to learn new frameworks and tools quickly. - Collaboration: Communicate effectively with technical and non-technical teams. Salary range: $90k - $105k. Application Deadline: The application window for this position is anticipated to close on March 15, 2026. We encourage you to apply as soon as possible. The posting may be available past this date but is not guaranteed. HMH is fully committed to Equal Employment Opportunity and to attracting, retaining, developing and promoting the most qualified employees without regard to race, gender, color, religion, sexual orientation, family status, marital status, pregnancy, gender identity, ethnic/national origin, ancestry, age, disability, military status, genetic predisposition, citizenship status, status as a disabled veteran, recently separated veteran, Armed Forces service medal veteran, other covered veteran, or any other characteristic protected by federal, state or local law. We are dedicated to providing a work environment free from discrimination and harassment, and where employees are treated with respect and dignity. We actively participate in E-Verify. #LI-VA1