About the Role:
Own product modules end-to-end from problem discovery to delivery translating business
needs into clear PRDs workflows acceptance criteria and rollout documentation.
Drive ML execution and quality oversee datasets training coordination evaluations error
analysis thresholds fallback logic and retraining triggers.
Prototype and validate AI ideas rapidly using LLMs agents and PoCs to test workflows
guardrails and feasibility before full-scale builds.
Ensure reliability and field readiness through rigorous validation edge-case testing RCAs and
graceful degradation under low-confidence scenarios.
Define success metrics and quality gates to ensure predictable performance minimal rework
and scalable AI solutions.
Collaborate cross-functionally with engineering data science business and customer teams to
align expectations and support adoption.
We are hiring an Associate Product Manager (APM) to build AI-driven enterprise products for
real-world use cases.
The role combines product ownership with hands-on ML and LLM prototyping.
Ideal for builders who turn experimental AI into reliable scalable workflows.
Who were looking for:
24 years of experience in Product Management or closely related roles ideally in applied
AI/ML or execution-heavy enterprise systems.
Ability to decompose ambiguous problem statements into clear requirements workflows
and delivery plans.
Comfort working with data including dashboards logs metrics and evaluation outputs
with strong attention to detail.
Exposure to AI-driven systems such as computer vision LLM/agent-based workflows or
speech/voice platforms is a strong plus.
Working knowledge of Python or SQL to support analysis validation or basic debugging.
Strong ownership mindset with high execution discipline and predictable follow-through.
Systems-oriented thinking with an understanding of AI uncertainty edge cases and real-
world operational constraints.
Clear written and verbal communication skills to document specifications and explain
complex behavior simply.
About the Role:Own product modules end-to-end from problem discovery to delivery translating businessneeds into clear PRDs workflows acceptance criteria and rollout documentation.Drive ML execution and quality oversee datasets training coordination evaluations erroranalysis thresholds fallback log...
About the Role:
Own product modules end-to-end from problem discovery to delivery translating business
needs into clear PRDs workflows acceptance criteria and rollout documentation.
Drive ML execution and quality oversee datasets training coordination evaluations error
analysis thresholds fallback logic and retraining triggers.
Prototype and validate AI ideas rapidly using LLMs agents and PoCs to test workflows
guardrails and feasibility before full-scale builds.
Ensure reliability and field readiness through rigorous validation edge-case testing RCAs and
graceful degradation under low-confidence scenarios.
Define success metrics and quality gates to ensure predictable performance minimal rework
and scalable AI solutions.
Collaborate cross-functionally with engineering data science business and customer teams to
align expectations and support adoption.
We are hiring an Associate Product Manager (APM) to build AI-driven enterprise products for
real-world use cases.
The role combines product ownership with hands-on ML and LLM prototyping.
Ideal for builders who turn experimental AI into reliable scalable workflows.
Who were looking for:
24 years of experience in Product Management or closely related roles ideally in applied
AI/ML or execution-heavy enterprise systems.
Ability to decompose ambiguous problem statements into clear requirements workflows
and delivery plans.
Comfort working with data including dashboards logs metrics and evaluation outputs
with strong attention to detail.
Exposure to AI-driven systems such as computer vision LLM/agent-based workflows or
speech/voice platforms is a strong plus.
Working knowledge of Python or SQL to support analysis validation or basic debugging.
Strong ownership mindset with high execution discipline and predictable follow-through.
Systems-oriented thinking with an understanding of AI uncertainty edge cases and real-
world operational constraints.
Clear written and verbal communication skills to document specifications and explain
complex behavior simply.
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