• Design and implement Machine Learning capabilities that improve Autodesk’s RAG platforms • Perform statistical and data analysis and exploration to generate datasets for model training and development • Collaborate with other members of the team to reach better solutions, and to position our team at the cutting edge of technology and ML practice • Provide technical leadership and mentorship for less-experienced members of the team to deliver key ML-powered features for our Digital Customer Platform. • Partner with stakeholders, to solve important business objectives across a range of problems • Translate business requirements and objectives into problems that can be solved with a combination of Data, Statistics, and Machine Learning

• Lead the AI Platform Enablement function as an operating domain with responsibility for roadmap, delivery, adoption, and operational strategy across multiple work-streams. • Define and execute the strategy for a governed internal AI platform environment that includes developer workspaces, secure gateway patterns, identity, observability, and enterprise integrations, in close partnership with the Enterprise AI team. • Partner with engineering, platform, infrastructure, security, risk, and compliance stakeholders to ensure the platform is secure, auditable, scalable, and aligned to enterprise standards. • Drive enablement programs that help internal teams adopt approved AI capabilities through structured training, reusable templates, starter kits, and rollout playbooks. • Build operating mechanisms, service models, and prioritization frameworks that help teams move from idea to working solutions in a controlled environment with speed and consistency. • Establish success metrics for platform usage, onboarding, adoption, delivery velocity, control effectiveness, and user experience. • Lead and grow a high-performing team, setting clear direction, developing talent, and creating strong cross-functional alignment across technical and business stakeholders. • Act as an executive-facing leader who can translate technical platform decisions into business outcomes, adoption plans, and risk-managed execution.

Role Description Our client is building the world’s first Human-Led AI Orchestration Layer—a platform designed to keep humans firmly in control of AI through clarity, accountability, and execution via our proprietary Action Points. As an AI Engineer, you will design and implement the intelligence layer that translates raw information into human-directed outcomes. You will work across LLMs, agentic workflows, and AI pipelines to ensure AI remains a tool governed by human intent, not an autonomous system. Everything you build is expected to be revenue-ready, secure, and production-grade. Key Responsibilities - AI & Intelligence Engineering - Design and implement AI-driven workflows that generate actionable, human-directed insights. - Build and optimize LLM-powered systems for summarization, recommendation, classification, and orchestration. - Implement agentic AI patterns that operate under explicit human guardrails and approval flows. - Translate unstructured data into structured Action Points™ aligned with user goals. - Develop and maintain prompt architectures and evaluation frameworks for consistent AI output quality. - Architect and develop semi-autonomous AI agents with mandatory human-in-the-loop control points, safety guardrails, and review checkpoints. - Voice to Text - Design and implement AI-driven voice workflows that generate actionable, human-directed insights. - Architect real-time speech-to-text pipelines for AI control, command execution, and Action Point generation. - Implement low-latency voice ingestion using Azure Speech SDK for real-time transcription, intent detection, and multilingual support. - Ensure voice inputs are securely processed, auditable, and aligned with human authorization and approval flows. - Translate spoken input into structured, human-directed Action Points that govern downstream AI agents. - Integration & Deployment - Deploy AI services using FastAPI and integrate them securely with backend systems. - Collaborate with frontend and backend engineers to expose AI capabilities via secure APIs. - Ensure AI systems scale reliably in cloud environments (Azure-based). - Performance, Ethics & Governance - Optimize inference latency, throughput, and cost efficiency. - Implement observability, logging, and monitoring for AI workflows. - Apply responsible AI principles: transparency, auditability, and human override by design. Qualifications - Strong experience in Python with AI/ML systems. - Hands-on experience with LLMs (OpenAI API or similar). - Experience deploying AI services with FastAPI. - Familiarity with agentic AI frameworks and orchestration patterns. - Experience working in cloud-based AI environments (Azure preferred). - Solid understanding of data processing, evaluation, and optimization. - Proficiency with Git and collaborative development workflows. Preferred Qualifications - Experience with vector databases (e.g., PGVector). - Familiarity with MLOps practices and AI CI/CD pipelines. - Experience with prompt engineering and evaluation frameworks. - Exposure to multi-tenant SaaS AI systems. Benefits - Flexible work structure of 20–40 hours per week, depending on role scope and workload. - Outcome-driven role, not hour-tracked. - Compensation as a fixed monthly stipend, aligned to responsibilities and expected ownership. - Stipend remains consistent as long as commitments are met and performance remains strong. - Environment requires clear ownership and follow-through, proactive communication, and consistent, high-quality delivery. - Flexibility is paired with accountability—team members are trusted to manage their time while ensuring outcomes, team continuity, and customer commitments are fully upheld.

• Own AI features end-to-end, from problem statement to GA • Move features through our rollout tiers (prototype → design partner → beta → GA), earning each promotion with an eval baseline and a capacity plan • Build and defend evals: golden datasets, a north-star metric per feature, and online monitoring — so we can *see and prove* how a feature behaves, not guess • Lead the "meaning" layer of our semantic stack: retrieval semantics, embedding quality (proven first on initiative-candidate retrieval), and the label flywheel that feeds prompt optimization and golden sets. You co-design our vector and knowledge-graph stores with the AI Platform team — you're their most demanding customer • Push output quality with the levers that matter: context assembly, prompt optimization, and per-customer/per-firm generation • Work closely with cross-functional teams — product managers, designers, and other engineers — to gather requirements, define measures of success, and deliver high-quality solutions • We want you not just to build but to *engage* with our product and our users. Your input and opinion will be highly valuable, and it is expected that you have many thoughts on how things should be done and why