Machine Learning Lead

at Process Nine Technologies

Machine Learning Lead

Process Nine Technologies

Company

Home

Machine Learning Lead

at Process Nine Technologies

Posted by Gobinda Patra

4 - 8 yrs

Best in industry

Gurugram

Skills

Machine Learning (ML)

Natural Language Processing (NLP)

TensorFlow

Deep Learning

PyTorch

Fine-tuning LLMs

LoRA / QLoRA

Speech-to-Text (STT)

Text-to-Speech (TTS)

OCR

MLOps

ML Leads JD

Key Responsibilities

Model Training & Fine-Tuning: Build, fine-tune, and optimize state-of-the-art NLP, LLM, Speech, and Vision models for scheduled Indian languages, utilizing parameter-efficient methods (LoRA, QLoRA, PEFT).
Indic Tokenization & Linguistics: Architect custom tokenizers and text-normalization pipelines to address the "fertility problem" in Devanagari, Dravidian, and other regional scripts, ensuring low-latency and cost-effective model inference.
Multimodal System Design: Develop robust OCR engines capable of parsing complex script geometries (conjoint consonants, Shirorekha, vowel modifiers) and integrate them into document intelligence pipelines.
Speech Engineering: Deploy and scale robust STT (Speech-to-Text) and TTS (Text-to-Speech) pipelines capable of handling heavy code-mixing (e.g., Hinglish, Tanglish), regional accents, and localized dialects.
Vernacular Guardrails & Evaluation: Establish culturally contextual benchmark datasets and implement safety guardrails.
Production Deployment (MLOps): Package and serve models using high-throughput frameworks (vLLM, Triton, ONNX) optimized for GPU environments, minimizing computational overhead for massive cross-lingual workloads.
Vernacular Fraud & Anomaly Detection: Architect risk-scoring systems and anomaly detection models capable of identifying fraud patterns in native scripts and code-mixed formats.

Essential Qualifications & Technical Skills

Education: Bachelor’s or Master's degree in Computer Science, Mathematics, Statistics, or a closely related quantitative field.
Experience: 4+ years of professional experience building and deploying machine learning models in production environments, with a proven track record in Indian Language NLP, Speech, or Anomaly Detection.
Programming: Expert-level proficiency in Python and standard ML frameworks (PyTorch, TensorFlow).
Indic AI Stack: Direct, hands-on experience with specialized Indic frameworks and datasets (e.g., AI4Bharat's IndicTrans2/IndicWhisper, Bhashini API, Kathbath, Sarvam-105B, or Aksharantar).
Fraud Stack: Proficiency in tabular/graph-based ML toolkits (XGBoost, LightGBM, PyTorch Geometric) and handling highly imbalanced target variables (SMOTE, class weights).
NLP & LLMs: Deep understanding of Transformer architectures, sequence-to-sequence modeling, cross-lingual embeddings, vector databases (Milvus, Pinecone, Qdrant), and quantization tools (bitsandbytes, GPTQ).
Speech & Vision Processing: Experience processing raw audio signals (grapheme-to-phoneme conversion, spectrogram analysis) or document structures using OCR networks (CRAFT, DBNet, LayoutLM).
Handling Code-Mixing: Proven ability to build models that gracefully parse text or speech containing heavy code-switching (mixed Latin/regional scripts, multi-language grammar).

ML Leads JD

Key Responsibilities

Model Training & Fine-Tuning: Build, fine-tune, and optimize state-of-the-art NLP, LLM, Speech, and Vision models for scheduled Indian languages, utilizing parameter-efficient methods (LoRA, QLoRA, PEFT).
Indic Tokenization & Linguistics: Architect custom tokenizers and text-normalization pipelines to address the "fertility problem" in Devanagari, Dravidian, and other regional scripts, ensuring low-latency and cost-effective model inference.
Multimodal System Design: Develop robust OCR engines capable of parsing complex script geometries (conjoint consonants, Shirorekha, vowel modifiers) and integrate them into document intelligence pipelines.
Speech Engineering: Deploy and scale robust STT (Speech-to-Text) and TTS (Text-to-Speech) pipelines capable of handling heavy code-mixing (e.g., Hinglish, Tanglish), regional accents, and localized dialects.
Vernacular Guardrails & Evaluation: Establish culturally contextual benchmark datasets and implement safety guardrails.
Production Deployment (MLOps): Package and serve models using high-throughput frameworks (vLLM, Triton, ONNX) optimized for GPU environments, minimizing computational overhead for massive cross-lingual workloads.
Vernacular Fraud & Anomaly Detection: Architect risk-scoring systems and anomaly detection models capable of identifying fraud patterns in native scripts and code-mixed formats.

Essential Qualifications & Technical Skills

Education: Bachelor’s or Master's degree in Computer Science, Mathematics, Statistics, or a closely related quantitative field.
Experience: 4+ years of professional experience building and deploying machine learning models in production environments, with a proven track record in Indian Language NLP, Speech, or Anomaly Detection.
Programming: Expert-level proficiency in Python and standard ML frameworks (PyTorch, TensorFlow).
Indic AI Stack: Direct, hands-on experience with specialized Indic frameworks and datasets (e.g., AI4Bharat's IndicTrans2/IndicWhisper, Bhashini API, Kathbath, Sarvam-105B, or Aksharantar).
Fraud Stack: Proficiency in tabular/graph-based ML toolkits (XGBoost, LightGBM, PyTorch Geometric) and handling highly imbalanced target variables (SMOTE, class weights).
NLP & LLMs: Deep understanding of Transformer architectures, sequence-to-sequence modeling, cross-lingual embeddings, vector databases (Milvus, Pinecone, Qdrant), and quantization tools (bitsandbytes, GPTQ).
Speech & Vision Processing: Experience processing raw audio signals (grapheme-to-phoneme conversion, spectrogram analysis) or document structures using OCR networks (CRAFT, DBNet, LayoutLM).
Handling Code-Mixing: Proven ability to build models that gracefully parse text or speech containing heavy code-switching (mixed Latin/regional scripts, multi-language grammar).

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About Process Nine Technologies

Founded :

2010

Type :

Product

Size

Stage :

Raised funding

About

Process9 is a localization technology provider enabling businesses to reach Indian language consumers and effectively increase their market share by providing them with innovative solutions to increase scalability.

Process Nine Technologies's video section

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