Solutions Consultant

Conquer AI builds AI systems that operate in real enterprise environments, not demos and not experiments. Our clients run complex, regulated businesses across financial services, healthcare, travel, and manufacturing, including Fortune 500 organisations.

The work is intentionally hard. We automate high-value workflows using agent-based architectures, extraction-first pipelines, and a mix of large and small language models. Accuracy, explainability, and operational reliability matter more than novelty.

We are looking for an AI Solutions Lead who wants ownership of client outcomes, not just project delivery. This role sits at the intersection of technical design, client engagement, and commercial judgement.

You will be responsible for shaping how AI solutions are scoped, sold, and delivered into production. That includes understanding client problems, translating them into viable technical approaches, owning the delivery, and ensuring systems work after launch. This is not a consulting role and not a sales engineering function. The expectation is that you own the full arc from discovery to deployment and beyond.

You will report to the Head of AI Solutions and operate as the primary technical lead on client engagements.

• Leading discovery and scoping for new AI solutions with enterprise clients
• Defining solution architectures that balance capability, risk, and delivery timelines
• Deciding what to build in-house, what to integrate, and what not to automate
• Translating ambiguous business problems into concrete technical requirements
• Designing systems that meet regulatory and operational constraints
• Working directly with engineering teams to ensure delivery matches intent
• Owning post-deployment performance and iterating based on real-world usage
• Building trusted relationships with senior stakeholders in client organisations

• Significant experience leading technical solutions in AI, ML, or complex software delivery
• Strong architectural and systems thinking with comfort making high-stakes trade-offs
• Hands‑on experience deploying AI or ML systems into production environments
• Ability to operate credibly with both C‑suite stakeholders and engineering teams
• Track record of owning outcomes, not just outputs
• Comfort navigating ambiguity and moving forward with incomplete information
• Strong communication skills and ability to explain technical decisions to non-technical audiences

Experience in regulated industries is useful but not required. Clear thinking, accountability, and client trust matter more.

• Autonomous teams with real ownership
• Solutions teams embedded with clients and engineering
• Direct accountability for what ships and what stays live
• Bias toward pragmatic delivery over theoretical perfection

• Ownership of complex, high-impact client engagements
• Real problems without artificial constraints
• Work that goes live and continues to operate in production
• High‑trust environment with genuine autonomy