Business Development Director – Artificial Intelligence

Front End is a Saudi-born, globally respected technology and industrial innovation company leading the future of energy, industry, and beyond. Recognized as Innovator of the Year at the Oil & Gas Middle East 2025 Awards and trusted by tier-one clients, we deliver a precision‑engineered ecosystem of advanced solutions across energy, industrial facilities, geoscience, offshore, space, and mining — fully aligned with Saudi Vision 2030.

Our Artificial Intelligence practice delivers mission‑critical, domain‑specific AI — not generic models. We integrate predictive analytics, operational optimization, anomaly detection, and autonomous decision systems directly into industrial, energy, and subsurface operations, combining real‑time data fusion, machine learning, and advanced modeling to drive faster, smarter, and more resilient decisions in high‑stakes environments.

This is a full‑time, on‑site role in Al Khobar for a Business Development Director – Artificial Intelligence. The Director will own the growth strategy, pipeline, and revenue for Front End's AI practice within the energy, oil & gas, and industrial sectors — translating our domain‑specific AI capabilities into commercial solutions for upstream, midstream, downstream, and industrial clients across the Kingdom and wider region.

This is a domain‑led commercial role, not a generalist technology sales position. The successful candidate must be able to sit credibly across the table from a reservoir engineer, a refinery operations manager, or a drilling superintendent, understand their technical and operational pain points, and articulate precisely how AI‑driven predictive analytics, process optimization, and anomaly detection create measurable value in their environment.

Candidates whose experience is purely in AI, SaaS, or software sales — without genuine oil & gas, subsurface, or industrial domain depth — will not be a fit for this role.

• Define and execute the business development strategy for Front End's AI offerings across the energy, oil & gas, and industrial process automation markets
• Build and convert a qualified pipeline with upstream, midstream, downstream, and industrial/manufacturing clients, including national oil companies, operators, and tier‑one industrial accounts
• Translate domain problems — reservoir and subsurface workflows, drilling and completions efficiency, refinery and process optimization, asset integrity, predictive maintenance — into scoped AI solutions and commercial proposals
• Partner with technical and delivery teams to shape solution architecture, proofs of concept, and use cases grounded in real operational data and outcomes
• Lead complex, consultative, multi‑stakeholder sales cycles involving engineering, operations, procurement, and C‑suite decision‑makers
• Develop and manage strategic partnerships and channel relationships that extend Front End's AI reach in the energy and industrial space
• Own commercial negotiations, contracting, and revenue targets for the AI practice, working closely with finance and legal
• Represent Front End as a credible domain authority at industry forums, client engagements, and technical discussions

• Minimum 10+ years in business development, commercial, or technical‑commercial roles, with a substantial and verifiable portion in energy, oil & gas, or subsurface — this is a hard requirement
• Demonstrable technical fluency in at least one core domain: upstream operations and subsurface workflows (seismic, reservoir characterization, drilling, completions), downstream and refinery/process operations, or industrial process automation for manufacturing (DCS/SCADA/PLC, process control, asset performance)
• Proven ability to map AI and data capabilities — predictive analytics, machine learning, anomaly detection, optimization — onto specific oil & gas and industrial use cases and quantify their business impact
• Track record of closing complex, high‑value B2B deals within the energy or industrial sector in Saudi Arabia or the GCC
• Strong command of the technical vocabulary, operating constraints, regulatory context, and decision‑making structures of…