Dura Digital
AI Engineer
About this role
About Dura Digital Dura Digital is a global digital transformation and consulting company that helps organizations turn emerging technologies into practical business outcomes. We bring together strategy, human-centered design, engineering, and AI expertise to create solutions that improve operations, customer experiences, and organizational performance. As a Dura Digital consultant, you will represent our commitment to thoughtful innovation, strong client partnership, and high-quality delivery while working closely with the client’s leadership and delivery teams. About the role We're hiring an AI Engineer to design, build, and ship AI-powered features that go into production and stay there. This is a hands-on engineering role, not a research role. You'll work across the full path from problem framing to deployment: turning an ambiguous business need into a working system, choosing the right model and retrieval approach, building the evaluation harness that proves it works, and standing behind it once real users depend on it. You'll partner closely with product, design, data, and platform engineers, and depending on the engagement, directly with client stakeholders who are new to AI and need a clear-eyed guide on what's realistic.
What you'll do
- Build and deploy production AI features: LLM-backed workflows, retrieval-augmented generation, agentic and tool-using systems, classification and extraction pipelines, and traditional ML models where they're the better fit.
- Own the data path behind those features: ingestion, chunking, embedding, indexing, and retrieval quality, and improve it based on measured outcomes rather than intuition.
- Design evaluation before you ship. Define what "good" means for each use case, build offline eval sets and online feedback loops, and track regressions as prompts, models, and data change.
- Integrate AI capabilities into existing applications and services through well-designed APIs, with attention to latency, cost per request, failure modes, and graceful degradation.
- Instrument and monitor deployed systems for quality drift, hallucination rates, token spend, and abuse, and act on what you see.
- Apply responsible-AI practices in day-to-day work: data privacy and residency, PII handling, prompt injection and data exfiltration risks, access control, and clear documentation of model limitations.
- Prototype quickly to reduce uncertainty, then make a deliberate call on what deserves to be hardened and what should be thrown away.
- Contribute to internal standards, reusable components, reference architectures, and patterns other teams can adopt.
- Explain technical tradeoffs to non-technical audiences, including client stakeholders and executives, without overselling what the technology can do. What you'll bring