AI Workflow Engineering Architect
On-site | Abu Dhabi
AppliedAI, founded in 2021, is a pioneering AI technology company headquartered in Abu Dhabi, UAE. We are committed to innovation and excellence in artificial intelligence solutions across regulated industries such as healthcare, insurance, government, and financial services.
Our flagship platform,
Opus
, automates and supervises mission-critical, document-heavy processes with embedded governance, auditability, and human oversight. We help enterprises achieve measurable productivity gains while increasing operational reliability and trust.
The AI Workflow Engineering Architect is the senior technical authority for the design and optimization of production workflows within Opus, with particular responsibility for ML-intensive and computationally demanding business processes.
The role establishes how Opus workflows should be engineered to achieve the required accuracy and reliability at the lowest practical inference cost and latency, using the capabilities available within Opus and supporting systems.
You’ll lead the technical development of Workflow Engineers through hands-on design work, architecture reviews, training, reference implementations and systematic analysis of production performance.
As the custodian of workflow engineering practice within AppliedAI, you’ll be responsible for converting advances in ML and computer science, and lessons from production, into standards, methods and reusable engineering patterns across the team.
Key responsibilities:Workflow architecture and optimization:
- Lead the decomposition of business processes into efficient Opus workflow graphs, defining execution boundaries, dependencies, state, parallelism and failure paths.
- Formulate important workflow-design decisions as constrained optimization problems across accuracy, latency, throughput, inference cost and operational reliability, and establish the appropriate operating point rather than optimizing any metric in isolation.
- Establish the technical methods by which Workflow Engineers select and compose models within Opus.
- Design and review heterogeneous execution strategies including deterministic computation, specialist models, retrieval, model cascades, conditional routing, early exits and human review.
- Require model choice and routing decisions to be supported by measured error rates, calibrated confidence, workload characteristics and marginal inference economics.
- Define the engineering standards by which Opus workflows are demonstrated to work.
- Establish representative test sets, business-weighted loss functions, component and end-to-end benchmarks, ablation methods, confidence intervals and regression thresholds.
- Ensure evaluation covers distribution shift, rare cases, correlated failures and high-cost errors rather than relying on aggregate model accuracy or successful demonstration cases.
- Lead technical analysis of workflow execution cost and performance, including model inference, context construction, retrieval, serialization, network calls, concurrency and orchestration overhead.
- Establish methods for profiling and improving caching, batching, parallel execution, model size, quantization, context length and accelerator utilization.
- Require performance to be characterized under realistic concurrency using throughput, cost per successful execution and p50/p95/p99 latency.
- Establish best practice for reliable Opus workflow execution, including typed interfaces, explicit state transitions, idempotency, checkpointing, bounded retries, timeouts, back pressure, compensation and…
(If this job is in fact in your jurisdiction, then you may be using a Proxy or VPN to access this site, and to progress further, you should change your connectivity to another mobile device or PC).