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Engineering

AI Engineer

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 your own measures
Adv

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