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Senior Deep Learning Engineer

NanoNets
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Hybrid (Bengaluru, KA, IN)INR 400k - INR 650kFull-timePosted 11mo ago

About the role

Senior Deep Learning Engineer at NanoNets focusing on production-ready deep learning solutions for document AI, including training/fine-tuning state-of-the-art architectures, production ML infrastructure, and agentic OCR capabilities. Role emphasizes scaling models, multi-modal learning, and cost-efficient inference in an enterprise context.

Responsibilities

  • Train & Fine-tune SOTA Architectures: Adapt and optimize transformer-based models, vision-language models, and custom architectures for document understanding at scale
  • Production ML Infrastructure: Design high-performance serving systems handling millions of requests daily using frameworks like TorchServe, Triton Inference Server, and vLLM
  • Agentic AI Systems: Build reasoning-capable OCR that goes beyond extraction – models that understand context, chain operations, and provide confidence-grounded outputs
  • Optimization at Scale: Implement quantization, distillation, and hardware acceleration techniques to achieve fast inference while maintaining accuracy
  • Multi-modal Innovation: Tackle alignment challenges between vision and language models, reduce hallucinations, and improve cross-modal understanding using RLHF and PEFT
  • Engineering Responsibilities:
  • Design distributed training pipelines for models with billions of parameters using PyTorch FSDP/DeepSpeed
  • Build comprehensive evaluation frameworks benchmarking against GPT-4V, Claude, and specialized document AI models
  • Implement A/B testing infrastructure for gradual model rollouts in production
  • Create reproducible training pipelines with experiment tracking
  • Optimize inference costs through dynamic batching, model pruning, and selective computation

Qualifications

  • 3+ years of hands-on deep learning experience with production deployments
  • Strong PyTorch expertise – ability to implement custom architectures, loss functions, and training loops from scratch
  • Experience with distributed training and large-scale model optimization
  • Proven track record of taking models from research to production
  • Solid understanding of transformer architectures, attention mechanisms, and modern training techniques
  • B.E./B.Tech from top-tier engineering colleges

Skills

Required:Deep learningPyTorchDistributed trainingTransformer architectures
Last updated Oct 14, 2026. Interested? Apply on the company site.
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About NanoNets

Automatic Data Extraction

Nanonets is an AI-driven solution that automates document processing and data extraction workflows. Leveraging advanced Optical Character Recognition (OCR) and deep learning models, Nanonets helps companies automate document-heavy business processes like accounts payable, order processing and insurance underwriting. Nanonets processes unstructured documents such as invoices, receipts, purchase orders, contracts, claims, and forms and converts them into structured output.

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