Description :

Devnagri is seeking an experienced Senior NLP/AI Engineer to lead development of cutting-edge AI systems powering translation, transliteration, ASR, OCR, LLM, and RAG products serving enterprise and government clients. This role requires expertise in building, optimising, and deploying production-grade NLP and speech models at scale, along with strong infrastructure, model engineering, and cross-functional collaboration skills.

You will be responsible for designing innovative AI solutions, training and optimising models, improving system performance, and ensuring high availability of AI services used across Devnagri's platform, chatbot systems, and enterprise AI deployments.

Responsibilities :

- Train, finetune, and deploy models across multiple domains : Multilingual Neural Machine Translation (NMT), Adaptive Translation Systems, Multilingual Transliteration models (Indian languages), Speech-to-Text (ASR / Whisper / Nvidia Nemo / Indic-ASR), Text-to-Speech (TTS), Large Language Models (LLMs), Embedding models for RAG.

- Build multilingual models supporting 20+ Indian languages.

- Perform dataset creation, preprocessing, augmentation, and large-scale training.

- Conduct model benchmarking using chrf++, BLEU, WER, CER, and custom evaluation metrics.

- Convert models to optimized inference formats (CTranslate2 Faster-Whisper, AWQ/INT4/INT8 quant).

- Reduce model sizes through quantisation and pruning.

- Optimise inference speed improvements for real-time workloads.

- Optimise GPU/CPU utilisation and memory footprint for large models.

- Build scalable inference pipelines for translation, ASR, and RAG.

- Develop advanced audio transcription and translation pipelines.

- Implement real-time STT systems for Indic languages.

- Build video subtitle extraction and SRT translation workflows.

- Integrate diarization, language detection, summarisation, and cross-lingual translation.

- Architect multilingual Retrieval-Augmented Generation (RAG) pipelines.

- Build vector databases and embedding models.

- Implement document indexing, chunking, parsing, and hybrid retrieval search.

- Integrate LLMs (Llama, Gemma, Qwen, etc. ) for chatbot and voice-bot systems.

- Manage AI/ML servers on AWS and GCP (GPU VM provisioning, optimisation).

- Reduce infra cost by optimising GPU usage, scheduling, and server consolidation.

- Implement auto-restart, monitoring, logging, and fail-safe mechanisms for all AI services.

- Deploy high-availability APIs for translation, transliteration, ASR, OCR, and chatbots.

- Familiarity with cloud-based GPU environments and troubleshooting (NVIDIA drivers).

- Work with Sales, Ops, and Tech teams to troubleshoot, support clients, and deliver large projects.

- Maintain detailed documentation for product flows, APIs, and model deployments.

- Handle urgent escalations, server crashes, and mission-critical deployments.

- Create internal tools and FAQs to reduce dependency on the AI team.

Requirements :

- Strong background in NLP, Speech, Deep Learning, and Generative AI.

- Experience : 4-5 years in production ML/NLP systems.

- Hands-on experience with : Python, PyTorch, TensorFlow, Speech to text and Text to speech models, open source LLMs, Transformer architectures, CTranslate2 Faster-Whisper, ONNX Runtime, LLM inference frameworks like vLLM, Sglang, LLM quantisation techniques, Vector DBs (FAISS, Pinecone), Docker, FastAPI, Linux systems, AWS/GCP GPU Infrastructure.

- Expertise in multilingual NLP, especially Indian languages.

- Experience creating datasets and training models from scratch.

Bonus Skills :

- Experience with WebRTC or real-time streaming protocols.

- Frontend basics for AI demo dashboards (Streamlit/Gradio).

- Knowledge of TTS, voice pipelines, barge-in systems, or telephony APIs.

- Experience with NVIDIA NeMo or similar speech frameworks.

Soft Skills :

- Strong ownership and accountability.

- Excellent communication and documentation clarity.

- Ability to independently research, prototype, and deploy new systems.

- Strong prioritisation and deadline management.

- Ability to handle high-pressure production issues.