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About Us
continuous AI learning systems
We are expanding our AI product and solution management team in Hyderabad to build world-class demos, POCs, and AI-native solutions that transform industrial domains such as aerospace, refineries, rail, silicon engineering, and large-scale IT operations.
If you are a systems thinker who loves solving complex problems and building fast prototypes—even if you don’t have direct AI experience—we want you.
Role Overview
AI Product / Solution Managers
3-day AI demos
Key Responsibilities
1. AI-Native Product Design
- Translate domain problems into TEITL 2.0 workflows (TrendIQ → DecisionIQ → ActionIQ).
- Design solutions that automate detection, decision-making, and operational actions.
- Build conceptual and UX workflows showing how users move through detect→decide→act loops.
- Explore problem statements deeply and create ‘Art of the Possible’ scenarios that inspire innovative AI solutions.
2. Rapid Demo & Prototype Development
- Deliver
working AI demos within 3 days
using ChatGPT, Claude, Figma, Make.com, Zapier, or quick scripting. - Create functional prototypes showing real value—not just PowerPoints.
- Work with LLMs to create intelligent flows, copilots, agents, or decision engines.
3. Domain Problem Solving
Work across industrial and engineering sectors such as:
- Aerospace component manufacturing
- Oil & Gas Refinery IOW monitoring & uptimeoperations improvement
- Rail predictive maintenance
- Silicon engineering yield optimization
- Data center operations and IT incident response
Translate domain constraints problem statements and constraints into actionable AI workflows.
4. Orchestration & Systems Thinking
- Design end-to-end workflows involving sensors, data streams, humans, and automation.
- Define events, actions, owners, and learning loops.
- Work closely with engineering to convert conceptual flows into production capabilities.
5. Customer & Stakeholder Engagement
- Effectively package and communicate AI solutions—transform technical workflows into compelling narratives for customers and internal teams.
- Lead workshops, discovery sessions, and solutioning meetings with global clients.
- Present demos clearly and articulate business value.
- Gather requirements through conversations, not checklists—bring structure to ambiguity.
6. Strategy, Roadmapping & Value Definition
- Build mini-roadmaps for AI feature development.
- Define measurable success criteria tied to operational impact.
- Identify opportunities for continuous learning loops within client processes.
Required Experience (Even Without Direct AI Background)
not
1. Technical & Systems Background
- Degree in Engineering, Computer Science, Information Systems, or similar.
- Experience with APIs, automation tools, cloud basics, or scripting.
- Comfort with understanding telemetry, operational data, workflows, or integration patterns.
2. Hands-On Problem Solving
- Ability to break down complex problems and explain them in simple terms to colleagues, team members, and customers.
- Experience building scrappy tools, automations, prototypes, or internal apps.
- Participation in innovation projects, hackathons, or 0→1 initiative building.
- Strong aptitude for understanding how systems work end-to-end.
- Demonstrates strong work ethic and ability to dive hands-on into problem statements, performing deep work to uncover insights and craft solutions.
3. Complex Cross-Functional Work
- Delivered projects involving multiple teams—Ops, Engineering, Quality, Maintenance, etc.
- Dynamic and adaptable—able to handle multiple projects and deadlines simultaneously while maintaining focus and delivering high-quality outcomes.
- Experience in industries such as manufacturing, energy, transportation, aerospace, IT ops, or similar (preferred but not required).
4. Communication & Visualization Skills
- Ability to create workflows, diagrams, and solution slides.
- Ability to create compelling solution slide decks with clear narratives that connect technical design to business value.
- Ability to articulate complex solutions in simple, clear points for non-technical audiences.
- Comfortable presenting to leadership and customers.
Preferred, But Not Required
- Exposure to LLMs, ChatGPT, Claude, or automation tools.
- Experience working in an industrial or engineering domain.
- Familiarity with industrial systems (SCADA, MES, Historian).
- Experience with time-series or event-driven systems.
- Basic understanding of ML concepts (correlation, anomaly detection, drift).
- Optional experience with enterprise tools such as ERP, CRM, or SCM systems.
- Flexibility to work shifted hours overlapping with U.S. time zones for global collaboration
