About Zinnov
Zinnov is a global management consulting firm that helps organizations make decisions that actually get used — and deliver results that matter. For over two decades, we’ve partnered with leading enterprises, high-growth technology companies, and investors to answer some of the toughest questions they face:
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Where should we invest?
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How do we scale globally?
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What capabilities will win in the next decade?
Our work shapes market entry strategies, global operating models, M&A decisions, and long-term growth bets. We’re known for being data-led, execution-focused, and outcome-driven — not opinion-heavy slideware.
At Zinnov, how you work matters as much as what you deliver. We value independent thinking, crisp communication, and early ownership. With 450+ professionals across 10 global offices, we work across industries including Digital Services, ER&D, Enterprise Software, Semiconductors, Healthcare, BFSI, Automotive, Media & Telecom, and Private Equity.
Zinnov isn’t for everyone. It’s for people who want steep learning curves, honest feedback, and the chance to see their work influence real business decisions — not just presentations.
About the Role
As an Agent / AI Engineer, you will build the AI layer of the GCC Intelligence Platform—the conversational agent users interact with. You will own grounding, retrieval, persona routing, session state, and permission-aware querying so the agent stays accurate, secure, and tenant-safe.
What You’ll Do
LLM & Agent Integration
- Integrate LLM APIs (e.g., AzureOpenAIor equivalent) for conversational workflows.
• Build a grounding layer that anchors responses to real platform data (not model guesses).
• Maintain prompt templates across multiple personas and use-cases.
Retrieval, Permissions & Security Boundaries
- Implement intent classification and persona routing to the right KPI views.
• Build the API handler that sets DB sessionvariables so RLS/ABAC policies enforce correctly.
• Own JWT validation and permission registry lookups that control access.
Reliability & Cost Controls
- Build session state management, safe retries, and failure handling.
• Implement token budgeting, tracking, and enforcement per tenant.
• Improve accuracy baselines through structured evaluation and regression checks.
What You Bring
Qualifications & Experience
- 3+ years building with LLM APIs in production (OpenAI/Azure/Anthropic or similar).
• Experience shipping grounding/RAG systems to real users.
• Strong Python for agent logic, APIs, and prompt management.
• Understanding of how auth/session context interacts with database queries.
Key Skills
- Prompt engineering across user types and tasks.
• Accuracy-first mindset—grounded correctness as a constraint, not a feature.
• Strong debugging and evaluation discipline.
What Success Looks Like – Globalization Excellence (GE)
- Agent answers staygrounded—accuracyimproves and hallucinations reduce.
• Access is safe—tenant boundaries are respected every time.
• Persona routing feels natural—users get the right view, right detail, right format.
• Costs stay controlled—token governance and caching/optimization reduce overrun risk.
If you enjoy building secure, grounded AI agents that executives can trust, and shipping AI systems with strong evaluation discipline — this role offers both impact and progression.
Zinnov is an equal opportunity employer. We celebrate diversity and are committed to building an inclusive workplace. We welcome applications from individuals of all backgrounds, communities, and experiences.