AI Workflow Engineering Architect

About the role

AppliedAI, an AI technology company based in Abu Dhabi, is looking for an AI Workflow Engineering Architect. The role is the senior technical authority for designing and optimizing production workflows within its flagship platform Opus, especially ML-intensive and computationally demanding processes. It also leads the technical development of Workflow Engineers through hands-on design, architecture reviews, training and reference implementations.

Key responsibilities

• Decompose business processes into efficient Opus workflow graphs, defining execution boundaries, dependencies, state, parallelism and failure paths.
• Balance accuracy, latency, throughput, inference cost and reliability, and set the right operating point rather than optimizing any metric alone.
• Define how Workflow Engineers select and compose models, and design heterogeneous execution strategies such as deterministic computation, retrieval, model cascades, conditional routing, early exits and human review.
• Set engineering standards for evaluation: test sets, weighted loss functions, benchmarks, ablation methods, confidence intervals and regression thresholds, covering distribution shift and rare cases.
• Analyze workflow cost and performance, and improve caching, batching, parallel execution, quantization, context length and accelerator utilization.
• Establish reliable execution practices: typed interfaces, explicit state transitions, idempotency, checkpointing, retries, timeouts, backpressure and compensation, plus property-based testing and fault injection.
• Define how performance is measured after deployment, instrument workflows, and diagnose regressions.
• Mentor Workflow Engineers, run architecture reviews and post-mortems, and turn expert knowledge into reusable engineering patterns.

Required profile

• Senior-level applied ML and software engineering background, with hands-on experience designing and running production LLM/agentic systems.
• Demonstrated experience mentoring engineers and setting technical standards.
• Desirable: experience in a regulated industry (healthcare, finance, insurance, BPO), building a technical discipline from scratch, or published writing on ML systems.

Required skills

• Strong grounding in ML evaluation and systems performance (latency, cost, concurrency)
• Distributed systems fundamentals: reliability, failure handling and state management
• Desirable: multi-model orchestration (routing, cascades, fallback across providers such as Anthropic, OpenAI, Gemini)

What we offer

• Opportunity to shape the marketing analytics strategy at a leading AI company.
• Exposure to cutting-edge AI/ML applications in real-world business contexts.
• Competitive compensation, benefits and career advancement opportunities.