AI Lead Consultant

We are excited to find an experienced and motivated individual to join our team and help us drive a successful project outcomes.

Remote work mode, with travel for key client meetings and workshops

Canada onshore

Remote work mode, with travel for key client meetings and workshops

Experience:

12-15 years | Vacancy: 2

We are looking for AI Lead consultants.

As AI Lead Consultant you are the person who answers that question in the room. You sit where pre-sales consulting, engagement leadership and AI capability building meet, and you are accountable for turning executive ambition into a roadmap our delivery teams can actually build.

Day to day you will run discovery workshops with client leadership, size and prioritise AI use cases, and build the business cases and ROI models that get programmes funded. You will partner with Sales and practice leadership to shape proposals, pitches and RFP responses, then stay with the engagement as the onsite consulting lead so that what was sold is what gets delivered.

Between engagements you will sharpen our own toolkit - the frameworks, accelerators and playbooks that let the next team start further along - and you will mentor consultants and engineers growing into client-facing AI work.

Pre-Sales and Solution Consulting

• Partner with Sales and practice leadership to shape AI proposals, pitches and solution narratives for prospective and existing clients.
• Run discovery and ideation workshops with client stakeholders to surface goals, pain points, data readiness and organisational constraints.
• Own the AI solution story in RFP responses and bid defences, including scope, delivery approach, commercial shape and risk.
• Qualify opportunities honestly - recommend against use cases where the data, readiness or value case does not hold up.

Business Case and Roadmap Definition

• Build ROI models and business cases that quantify value, cost and payback period for each candidate use case.
• Sequence AI adoption roadmaps across quick wins, platform foundations and transformational bets.
• Define success measures with the client before build starts, so value realisation is measurable rather than anecdotal.

Engagement Leadership

• Act as the onsite engagement lead for AI consulting and implementation programmes, owning client confidence end to end.
• Hold the bridge between client and delivery - translate business intent into technical direction, and technical constraint into business language.
• Govern scope, risk and stakeholder alignment across the engagement, escalating early rather than late.

Capability and Practice Building

• Develop and refine proprietary AI frameworks, methodologies, accelerators and reusable toolkits.
• Mentor consultants and engineers moving into client-facing AI work, and run internal enablement sessions.
• Publish thought leadership - white papers, points of view and case studies - and represent Robosoft at client and industry forums.
• Track emerging AI technologies and regulation, and fold the implications into our frameworks and client advice.

• 12 to 15 years in consulting, digital transformation, or AI and analytics programmes.
• Proven pre-sales or solution consulting experience with executive-level stakeholders, including owning proposals and RFP responses.
• Demonstrated engagement or programme leadership on AI or analytics initiatives, ideally onsite with the client.
• Track record of building business cases and ROI models that secured programme funding.
• Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, Engineering, Business or a related discipline.
• Based in the Cannada, Remote working mode, with the ability to travel occasionally for key client meetings.

• Executive communication and storytelling - you can compress a complex programme into three slides and defend it under questioning.
• Stakeholder management across levels, from C-suite sponsors to the engineering leads who inherit the work.
• Workshop facilitation with mixed business and technical groups.
• Written fluency - proposals, points of view and case studies that read as authored, not assembled.

• AI and ML fundamentals - model lifecycle, evaluation, limitations, and when not to use ML.
• Generative AI and LLMs - RAG patterns, agentic workflows, prompt and context design, model selection trade-offs.
• MLOps and AI governance - deployment, monitoring, model risk, responsible-AI practice and emerging regulatory obligations.
• Business case and ROI modelling for technology investments.
• Pre-sales…