Functional Consultant- AI

Discover and shape AI use cases Run discovery workshops with business and technical stakeholders, and separate the AI ideas worth building from the ones that aren't ready. Assess feasibility on the merits — data availability, process fit, risk, cost Design the AI experience (UX) Define how people interact with the AI: conversational and agent flows, copilot interaction patterns, how confidence and sources are surfaced, error and fallback states, and expectations around latency and streaming. Decide where the AI should be visible, where it should stay in the background, and where a human stays in control.

Partner with UI/UX designers to turn this into real interface design, and make sure the experience feels trustworthy and usable rather than gimmicky. Drive value and adoption Build the value case up front (effort saved, deflection, cycle-time, quality) and measure realisation after go-live. Own UAT and drive adoption — the solution only counts if people use it and trust it.

Drive go-to-market, presales and demos Deliver compelling solution and product demos to clients and prospects — build the demo storyline, stand up the environment or prototype, and tailor the narrative to the audience in the room. Produce client-facing collateral: pitch decks, capability and solution presentations, proposal content, one-pagers and supporting documents. Contribute to the AI department's go-to-market — positioning and messaging, accelerator and product launches, campaign material, and reusable demo and pitch assets the wider team can draw on.

Support scoping, effort estimation and solution storylines for AI engagements. Work across whatever stack fits the engagement: LLM APIs, agent frameworks, RAG pipelines, vendor copilots, or AI features inside enterprise platforms. What You Bring 4–10 years in functional consulting or business analysis, with recent hands-on work on AI or intelligent-automation solutions.

A real understanding of how modern AI behaves: LLMs, prompting, retrieval and grounding (RAG), agentic patterns and tool-calling, and a working sense of why models fail and how to mitigate it. A feel for AI UX — what makes a copilot, agent or assistant trustworthy and usable versus frustrating — and the ability to specify interaction design and collaborate with designers. Strong requirements elicitation, process mapping and documentation; you can write specs for systems whose outputs aren't fully deterministic.

A confident facilitator and stakeholder manager who can hold a room of senior people and move a workshop toward decisions. Genuinely product-agnostic instincts — you reason about the right approach, not a preferred one. Strong presentation and storytelling skills — you can deliver a credible demo to a prospect and produce decks and documents that hold up in front of senior buyers.

Some exposure to evaluating or testing AI outputs, and comfort with Agile delivery. Nice to Have Hands-on experience with one or more LLM platforms (OpenAI, Anthropic, Google, Azure OpenAI) or AI capabilities inside enterprise products. Conversational design or prompt-engineering depth.

Familiarity with evaluation frameworks, LLM observability, vector databases and retrieval, or agent interoperability standards such as MCP. Delivery experience in a regulated sector (BFSI, pharma, manufacturing) or in the GCC/KSA market. A background in presales, solution consulting or go-to-market, with a track record of demos and decks that moved deals forward.

A portfolio of AI experiences or products you've helped shape or ship.