Senior AIML Engineer

Resource Requirement — Senior AI/ML Engineer

2 Days WFO

The engineer is expected to support GTB in executing an independent, AI-assisted content safety review. The support would be required for building and deploying an independent detection engine to identify NSFW and inappropriate content across text chat, voice, and video interactions.

Work Requirements

The engineer will support in building, calibrating, and deploying GT's independent content review engine. The engagement would be using an LLM (frontier models) for classification. The engineer will be working with AI tooling extensively.

Specifically:

Reviewing the behaviour category taxonomy and translating it into a detection schema

Building a two-layer detection pipeline: regex and rule-based layer for pattern matching, followed by an LLM classification layer using LLM for contextual detection

Designing and integrating a voice tone analysis module for aggression, stress, and hostility detection from audio samples

Constructing and documenting the evaluation set labelling schema: the ground truth framework that analysts will use for manual annotation

Running calibration sessions with the analyst team to ensure annotation consistency

Validating engine output: precision, recall, false positive rate, threshold tuning

Orchestrating the full pipeline run against the sample data on client infrastructure

Preparing the benchmarking output between GT's engine and the client's existing detection system

Must-Have Skills

Strong Python — pipeline development, API integration, data processing

Hands-on experience with LLM APIs (Gemini, OpenAI, or equivalent) — prompt engineering, classification workflows, output parsing

Familiarity with coding agents and AI-assisted development workflows

Experience with text classification and NLP tasks

Basic audio processing — feature extraction using libraries such as librosa or equivalent; ability to integrate pre-trained audio models

Ability to work within hardware constraints (CPU-only, 8GB RAM)

Good to Have

Familiarity with multilingual content — the sample will contain multiple Indian languages alongside English

Experience building evaluation sets or ground truth datasets for ML systems

What the Engineer Does Not Need to Handle

Cloud infrastructure or DevOps

Frontend or UI development

Manual annotation