Agent / AI Engineer
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About the Role
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 – Global Excellence (GE)
- Agent answers staygrounded—accuracyimproves and hallucinations reduce.
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