Own the retrieval core and every AI feature in the product: turning dense privacy notices into something a person can understand, showing people what changed when an organisation revises its terms, and classifying and routing grievances within statutory timelines.
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Location
Greater Noida
• Hybrid
Type
Full time, permanent
Reports to
Head of Engineering
Experience
4+ years
Why this role
Build grounded AI inside a boundary the model is not allowed to cross.
01
Every output grounded in a source we can point at
02
Evaluation harness in CI, not in a spreadsheet
03
Twenty-two languages, with evaluation literature you will have to build
04
Adversarial by default — a consent system is a target
The work
What you'll actually do
01
What you'll own
• The RAG core: ingestion, chunking, embeddings, retrieval and reranking over privacy notices, policies and regulatory text
• Notice simplification — turning legal language into something an ordinary person understands without changing what it means
• Version diff: telling somebody in plain language what actually changed when an organisation updated its notice
• Grievance classification and routing, with statutory response clocks running so a misroute has a cost
• Grounding and auditability: source logging, citation, and an evaluation harness that is part of CI
• Guardrails — PII masking, injection defence, and knowing when the right output is a refusal
02
The hard part
• The model cannot see the data. Personal data routed through the platform must not be readable by us, which limits what can reach a model provider at all
• Wrong is not a quality metric here — if we simplify a notice and change its meaning, a person consents to something they did not agree to
• Twenty-two languages: notices may be required in English or any language in the Eighth Schedule, across scripts where you will have to build y
📌 AI Engineer (Karamba)
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