AI Product Manager

Sitting between business, product, and engineering, this role is about shaping how AI—especially LLMs—is applied to real workflows, decisions, and user experiences. With a strong focus on use-case design, evaluation, and adoption, you’ll help clients move from “AI sounds promising” to solutions that are trusted, usable, and valuable in day-to-day work.

Applied AI Product Specialist

Calimala partners with enterprises across the Gulf and Europe to design, build, and scale Data & AI teams. As an AI Product Manager, you’ll join a network of professionals who understand both the potential and the limitations of modern AI—and who know how to apply it responsibly inside complex organizational, regulatory, and operational contexts.

This role sits at the intersection of AI capabilities, business processes, and user experience. You’ll help design AI-powered workflows—often involving LLMs, retrieval, and decision support—across use cases such as copilots, document analysis, internal knowledge tools, and intelligent automation.

What you'll be doing

As an AI Product Manager at Calimala, you’ll support and lead engagements where AI must make sense to real users and stakeholders. One project might involve shaping an AI-assisted workflow for operations or compliance teams; another could focus on defining how an LLM-based assistant integrates into an existing product or process, including how it behaves when it’s uncertain or wrong.

“We treat AI features like any other product: clearly scoped, explainable, testable, and evaluated by the value they create — not by how advanced the technology sounds.”

You’ll work closely with domain experts and engineering teams to frame problems, identify where AI adds value (and where it doesn’t), and translate business needs into well-defined AI solution concepts. You’ll help design interaction patterns, success criteria, and evaluation approaches, and ensure AI features are understandable, trustworthy, and aligned with user expectations.

Who we're looking for

You’re comfortable operating in ambiguity and enjoy shaping solutions before they are fully defined. You can reason about AI behavior, limitations, and trade-offs without needing to own low-level implementation or infrastructure. You’re curious about how people actually use AI tools—and why they sometimes don’t.

You’ve likely worked in product, consulting, operations, analytics, or applied technology roles where AI or automation was introduced into real business processes. You may not consider yourself a hardcore engineer, but you’re technically literate: you can collaborate closely with engineers, understand system constraints, and contribute meaningfully to design and evaluation discussions.

At Calimala, we value depth, accountability, and partnership. You take ownership of AI solutions from problem framing through to adoption, ensuring they make sense for users and stakeholders—not just in demos.

Experience shaping or delivering AI-enabled solutions (LLM-based or otherwise) within real products, workflows, or internal tools

• Strong ability to translate business problems into AI use-cases, workflows, and interaction patterns

• Understanding of common LLM application patterns, such as retrieval-augmented generation (RAG), prompt-based workflows, and human-in-the-loop designs

• Comfort working with AI systems at a conceptual and applied level, even if you don’t write production code

• Familiarity with how AI models are consumed via APIs and how they integrate into products or processes

• Ability to reason about data dependencies, AI behavior, limitations, and failure modes

• Experience defining success criteria for AI features beyond accuracy (usefulness, trust, consistency, time saved)

• Awareness of AI UX considerations, including explainability, confidence calibration, fallback paths, and user feedback

• Sensitivity to risk, governance, and compliance considerations in enterprise AI use

• Strong communication skills and comfort working across product, engineering, data, and business teams

• Experience supporting or leading workshops, discovery sessions, or stakeholder discussions around AI solutions

• Bias toward clarity, practicality, and outcomes over hype

We’re looking for practitioners who see Applied AI as the discipline of making AI work for people and organizations. People who enjoy shaping solutions, asking hard questions about value and risk, and helping teams adopt AI systems they can actually trust and rely on — not just admire in a demo.