AI Engineer
Posted 6 Aug 2026
Advertised as “Lead AI Engineer ”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 7+ yrs
- Management level
- People management
No salary listed for this role.
Field of studyComputer Science, Data Science, Engineering (or related)
Study levelBachelor's or higher
Roboyo AI is a category shaper in Applied AI and Agentic Automation. We help leading enterprises move from automation projects and AI experiments into governed, production-grade systems that execute real work across workflows, processes, products and services.
Our heritage is in scaled enterprise automation; our future is Applied AI and Agentic Automation: autonomous agents, AI engineering squads, process orchestration, data and knowledge foundations, and human-in-the-loop governance embedded deeply into how our clients operate.
We are not here to sell AI theatre. We are here to turn AI into working systems, measurable outcomes and repeatable transformation programs.
About the Role
The AI Lead is responsible for overseeing the end-to-end implementation of Agentic AI and Intelligent Automation solutions for enterprise clients. This role requires a combination of technical expertise, team & project management proficiency, and business acumen to ensure the successful deployment of scalable Agentic AI solutions that deliver measurable business value. Additionally, the role is central to building, managing, and upskilling a high-performing technical delivery team.
Key Responsibilities
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Project Leadership: Manage the full delivery lifecycle of AI initiatives, from initial discovery and proof-of-concept (PoC) to full-scale production deployment.
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Team Management & Growth: Lead cross-functional teams comprising Data Scientists, AI Engineers, and Solution Architects. Responsibilities include defining KPIs, conducting performance reviews, and fostering a high-performance culture.
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AI delivery Practice Building: Drive the expansion of the AI delivery practice by identifying talent gaps, leading recruitment efforts, and managing the onboarding process for new technical hires.
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Upskilling & Mentorship: Establish structured mentorship programs and training paths to ensure the team remains proficient in emerging technologies, such as LLMOps, Agentic AI, and advanced model architectures.
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Stakeholder Management: Act as the primary technical point of contact for clients, translating complex technical concepts into clear business outcomes and managing executive expectations.
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Solution Integration: Collaborate with automation teams to integrate Agentic AI and Machine Learning models into broader Hyperautomation frameworks, including RPA and low-code platforms.