AI Engineer - Product & Adoption Capability

We at P99soft are looking for an applied AI engineering role that also requires GTM-type capability—the ability to think about how AI tools actually get adopted by end users, not a dedicated go-to-market or marketing title. The engineer is expected to understand user workflows, spot adoption barriers, and shape how tooling gets rolled out while remaining hands-on with applied AI work day to day.

Responsibilities:

• Build and support the rollout of AI-powered tools, driving awareness, onboarding, and adoption among target end users.
• Understand and map user workflows to identify where and why adoption of new AI tooling breaks down, and design solutions to close those gaps.
• Partner closely with engineering and architecture stakeholders to translate what's technically possible into what users will actually adopt and use.
• Gather and synthesize user feedback to inform product decisions and prioritization for the AI tooling roadmap.
• Apply hands-on applied AI knowledge to evaluate tool capability and limitations firsthand, rather than relying solely on engineering input.
• Communicate technical AI concepts to non-technical stakeholders in a way that drives buy-in and correct usage.

Requirements:

• Applied, hands-on AI experience is non-negotiable: genuine exposure to building or working closely with generative/agentic AI systems, not just using AI assistants as an end user.
• Working GTM-type capability: comfort defining requirements, prioritizing based on user needs, and driving adoption of a tool; this can come from prior product exposure rather than a formal GTM title.
• Strong user empathy and the ability to identify practical adoption barriers for enterprise tools, distinct from purely technical blockers.
• Comfort operating with generative/applied AI systems (LLMs, agentic tools) as distinct from traditional data science approaches (regression, classification).
• Background can come from startup, founder, or self-taught paths; a traditional data science or enterprise pedigree is not required, provided applied AI fundamentals are genuinely strong.

Nice to Have:

• Some depth in AI systems internals (e. g., exposure to debugging model-serving or library/version compatibility issues) is a plus, though this role does not require building custom training pipelines from scratch.
• Experience operating in a regulated, quality-driven, or highly technical enterprise environment.

Skills

AI, Agentic AI, Generative AI, LLM, LLMs, Large Language Models