Agentic AI Platform

6 days ago

Bengaluru, Karnataka, India Uberlife Consulting Full time ₹ 20,00,000 - ₹ 25,00,000 per year

REQUIRED CRITERIA
•

Bachelors degree in Computer Science, Engineering, Data Science, or equivalent practical experience.
• 6+ years in product management, including 2+ years delivering ML- or AI-powered features.
• Working knowledge of the ML lifecycle, with a strong emphasis on LLMs, prompt-engineering, and generative model evaluation techniques.
• Proven ability to lead and influence engineers, designers, researchers, and GTM stakeholders in a matrix environment.
• Exceptional written and verbal communication skills, comfortable translating between business and technical audiences.

PREFERRED EXPERIENCE


• Hands-on exposure to Generative AI and large-language-model architectures (e.g., RAG, finetuning, RLHF).
• Experience designing or managing agentic AI / agent-builder frameworks (e.g., LangChain, AutoGen, CrewAI) to orchestrate multi-step analytical workflows.
• Familiarity with AI governance frameworks and mitigating risks such as hallucination and bias in generative models.
• Experience with retailers, CPGs, or other data-rich verticals.
• Expertise in Agile/Scrum, product discovery frameworks, and design-thinking methodologies.

KEY RESPONSIBILITIES
•

Build and maintain the product vision, strategy, and multi-quarter roadmap for our Generative AI oBerings, aligning with company OKRs and financial targets.
• Deeply understand user problems through interviews, data analysis, and market research; translate insights into epics, user stories, and acceptance criteria for building sophisticated AI agents.
• Partner with data scientists and ML engineers to select or train the right foundation models, balancing performance, safety, latency, and cost.
• Define success both for users (adoption, retention, task success rate) and for models (hallucination rate, bias, guard-rail triggers).
• Design the full evaluation pipeline for generative modelsoBline benchmarks, synthetic-data tests, red teaming, human-in-the-loop reviews, and online A/B deployments—and iterate based on learnings.
• Instrument dashboards that track product KPIs and model-centric metrics in real-time; monitor for performance degradation and coordinate fine-tuning or rollback as needed.
• Prioritize and manage the product backlog, ensuring clear scope for each sprint and release while removing impediments that block delivery.
• Champion data governance, privacy, IP protection, and responsible-AI standards throughout the development process.
• Drive go-to-market readiness: craft compelling demo narratives, create enablement materials, support documentation, and partner with sales on key deals.
• Foster a culture of design thinking, rapid experimentation, and continuous improvement within a cross-functional, globally distributed team.

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