Senior Product Designer, Mobile
Own the observability and lifecycle management of AI features across the organization. Build tools and infrastructure to enable teams to develop, monitor, and optimize LLM-powered features. Design and implement closed-loop evaluation pipelines that automatically validate prompt changes. Develop comprehensive metrics and dashboards to track LLM usage including cost per feature, token patterns, and latency. Create systems that tie user feedback to specific prompts and LLM calls. Establish best practices and processes for the full lifecycle of prompts including development, testing, deployment, and monitoring. Collaborate with engineering teams to ensure they have the tools and visibility needed to build high-quality AI features.
Energy Engineering & Python Expert - Freelance AI Trainer
Contributors may design rigorous energy engineering 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; and apply structured scoring criteria to multi-step problems.
Mechanical Engineer & Python Expert - Freelance AI Trainer
Contributors 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, and apply structured scoring criteria to assess multi-step problem solving.
Statistics Expert (Python) - Freelance AI Trainer
Contributors may design rigorous statistics problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; validate calculations or simulations using Python libraries including NumPy, Pandas, SciPy, Statsmodels, and Scikit-learn; improve AI reasoning to align with industry-standard logic; and apply structured scoring criteria to multi-step problems.
Lazo - Head of Engineering
The Head of Engineering at Lazo is responsible for owning the technology strategy and roadmap aligned with business and product OKRs, defining the reference architecture for agentic systems including LLMs and tool orchestration, establishing security and compliance baselines such as IAM, data privacy, and SOC2-readiness, and managing cost governance (FinOps). They present trade-offs, risks, and progress in leadership reviews. The role involves hands-on engineering and delivery, including shipping backend services in Python/TypeScript, orchestrating agents and toolchains, integrating external APIs and databases, building robust pipelines, and handling end-to-end DevOps using AWS/GCP, containerization, IaC, CI/CD, and observability, as well as on-call design. They work to reduce technical debt, improve latency and throughput, and manage infrastructure cost per workflow/client. Responsibilities also include defining SLOs and error budgets to reduce MTTR and change-fail rates, implementing data access policies and secure data flows for AI features, driving post-mortems and preventive engineering, hiring and mentoring engineers, setting performance scorecards, fostering a culture of thoughtful trade-offs and fast feedback loops, partnering with Product and AI teams for scalable solutions, collaborating with Ops, Growth, and Customer teams for reliability and launch readiness, and managing key vendors and build-versus-buy decisions with ROI narratives.
Physics Researcher (Python) - Freelance AI Trainer
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, and apply structured scoring criteria to multi-step problems.
AceUp - Lead ML Engineer (Generative AI & LLM Focus)
The Lead ML Engineer is responsible for architecting conversational agents that maintain long-running coherent dialogues and handle complex reasoning tasks. They develop low-latency retrieval augmented generation (RAG) pipelines that ground LLM responses in proprietary data to ensure accuracy and minimize hallucinations. The role leads the development of NLP pipelines to extract structured insights from various unstructured data sources, implements advanced personalization layers that adapt model behavior based on user history and context, and owns the deployment lifecycle of models including prompt architecture, evaluation frameworks, latency optimization, and cost management on Vertex AI. Additionally, the engineer acts as a technical mentor by reviewing code, setting architectural standards, and guiding technical decisions without managing personnel.
Prospera AI - AI Backend Engineer
The AI Backend Engineer will own and evolve the LLM orchestration pipeline, including designing and optimizing the multi-agent orchestration system, implementing parallelization and streaming to reduce response latency, and building prompt management with versioning and A/B testing. They will design retrieval-augmented generation (RAG) systems for accurate contextual responses, work with vector databases, embeddings, and relevance scoring, and optimize for speed and accuracy at scale. The role involves developing production APIs to connect AI capabilities to the frontend, with considerations for authentication, rate limiting, documentation, and designing for future integrations with CRMs and advisor tools. Additionally, the engineer will establish code review practices and testing standards, document architecture decisions, and contribute to technical patents and intellectual property development.
Full Stack AI Engineer – BuilderEx
Design, build, and maintain full-stack applications powering identity and access management (IAM) experiences. Develop and integrate AI/ML models for identity use cases such as fraud detection, anomaly detection, risk-based authentication, and identity verification. Lead and execute SSO migrations across products and platforms, consolidating authentication flows while minimizing user disruption. Drive domain consolidation initiatives by unifying identity systems, services, and user data models across multiple platforms or brands. Improve developer experience by building internal tools, SDKs, APIs, and documentation that simplify identity integrations. Design and evolve secure, scalable APIs supporting authentication, authorization, and identity data services. Partner with Security, Platform, and Product teams to implement and standardize protocols and patterns such as OAuth 2.0, OpenID Connect, SAML, JWT, and zero-trust architectures. Ensure AI-powered identity systems are observable, explainable, and production-ready with robust monitoring and feedback loops. Balance security, performance, and usability while maintaining privacy and compliance. Contribute to architectural decisions, technical design discussions, and code quality standards.
Full Stack AI Engineer
Design, build, and deploy AI/ML solutions to automate ITSM ticket triage, classification, prioritization, and routing. Develop NLP-based models for ticket summarization, root-cause detection, and resolution recommendation. Implement AI-powered virtual agents / copilots to assist support engineers and end users. Partner with Product Support, SRE, and Engineering teams to understand recurring issues and automate resolution workflows. Build intelligent runbooks and self-healing automation for common incidents and service requests. Enhance knowledge management by auto-generating and updating KB articles from resolved tickets. Integrate AI solutions with ITSM platforms (HALO). Develop APIs, workflows, and event-driven automations across monitoring, logging, and ITSM tools. Ensure seamless handoff between AI systems and human support engineers. Analyze ticket, incident, and operational data to identify automation opportunities. Train, evaluate, and continuously improve ML models using real-world support data. Implement monitoring for model performance, drift, and accuracy in production. Ensure AI solutions meet reliability, security, and compliance standards. Implement guardrails, explainability, and auditability for AI-driven decisions. Contribute to AI governance and responsible AI practices.
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