Staff Engineer (Applied AI / ML)

**Who We Are** Qvest US is the global leader in technology and business consulting for the Media & Entertainment and Consumer Packaged Goods & Retail industries. We strategize, advise, design, develop and implement future-forward business & technology solutions. With expertise in digital media supply chain, data & analytics, IP & rights management, broadcast transformation, Salesforce and applied AI, our exceptionally talented teams partner with Fortune 1,000 companies to revolutionize markets and set new industry standards.

About The IDC The IDC is our I nnovation and D evelopment C enter. We build custom software using modern technologies to solve complex business problems for our clients. We believe the best products and services are realized by knowledgeable, tight-knit teams aligned to a shared purpose and understanding.

Who We’re Seeking The Staff AI Engineer is a dual role combining technical leadership with hands-on AI engineering. Operating at the intersection of business strategy and technical execution, this role is responsible for interfacing directly with client business stakeholders to identify, evaluate, and prioritize high-value AI opportunities - and then leading the hands-on engineering to build and deploy those solutions.

In this role, you will engineer agentic systems - open-loop, conversational, tool-calling, and beyond, integrate AI and machine learning models with custom enterprise AI workflows, create rigorous evaluation frameworks, and deliver end-to-end, production-ready AI products. You will also support facilitation of discovery workshops, map operational pain points into structured AI specifications, and rapidly move from concept to working prototypes and production-grade software.

Rapid prototyping skills are essential, but we are not looking for someone who only knows how to vibe-code flashy demos. Successful candidates will keenly appreciate the difference between a proof of concept and a production system, know what level of engineering rigor each stage demands, and be capable of owning the full journey from prototype through production.

What You’ll Do **3. LLMOps, Governance & Production Hardening**

• AI Discovery & Technical Product Leadership
• Stakeholder Ideation: Support client discovery workshops, whiteboarding sessions, and interviews with client business and product owners to identify operational bottlenecks suitable for AI intervention. Successful candidates will be comfortable engaging directly and frequently with the client, demoing work early and often, gathering feedback, and closing tight iterative loops throughout the development process.
• Use Case Prioritization & ROI Analysis: Evaluate potential AI use cases across business impact, technical viability, data readiness, and ROI to build prioritized product roadmaps and PoC scopes. Recommend pragmatic alternatives when the requested approach is unlikely to be the simplest, highest-value, or most effective solution.
• Requirements to Spec Translation: Bridge the gap between vague business asks and clear technical architecture, translating functional requirements into PRDs, technical specifications, and verifiable prompt/eval criteria. Constructively challenge proposed solutions when appropriate and recommend simpler or more effective technical approaches.
• Rapid Prototyping (0-to-1): Rapidly build and demonstrate working proof-of-concept (PoC) apps and interactive demos (within 2–3 week cycles) to validate value with local executive stakeholders before full investment.
• Prototype-to-Production Hardening (1-to-N): Evolve successful prototypes into reliable, secure, maintainable production systems by strengthening architecture, testing, observability, scalability, evaluation, and operational readiness while preserving delivery velocity.
• Architecture & Core AI Engineering
• Agentic Systems & Harness Design: Design and build robust single- and multi-agent systems, including tool use, control loops, context and state management, planning, delegation, structured outputs, and failure recovery. Build the harnesses and runtime primitives that make agent behavior reliable, inspectable, and maintainable.
• Full-Stack Product Engineering: Work comfortably across the stack - from front-end experiences and backend services to APIs, data infrastructure, and cloud deployment - to get software shipped. You do not need to be a specialist at every layer, but you sho…