Deep Learning Engineer

NanoNets
Bengaluru
Workplace: HybridFull timeFunction: Data Science & Machine LearningExperience: 5-8 yearsSkills: ["Continuous learning","Experimentation","Ability to rapidly learn","Software engineering practices"]

Build and deploy generalized deep learning architectures to solve complex business problems such as converting unstructured data into structured outputs without hand-tuned features. You’ll develop state-of-the-art models, run continuous experiments, and incorporate new research into production-grade systems at scale. Work on advanced areas like LLMs/VLMs and multimodal learning, with strong engineering practices including CI/CD, version control, and code quality.

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FursaFursa
NanoNets
NanoNets
3 weeks ago

Deep Learning Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 1 hour agoStatus: Live
Reposted: similar role first listed 11 months ago

Job Summary

Build and deploy generalized deep learning architectures to solve complex business problems such as converting unstructured data into structured outputs without hand-tuned features. You’ll develop state-of-the-art models, run continuous experiments, and incorporate new research into production-grade systems at scale. Work on advanced areas like LLMs/VLMs and multimodal learning, with strong engineering practices including CI/CD, version control, and code quality.
Location: Bengaluru
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Build and deploy generalized deep learning architectures to solve business problems like converting unstructured data into structured format.
  • •Develop state-of-the-art models and continuously experiment with new approaches and advancements.
  • •Incorporate research into production-grade deep learning systems that operate at scale.
  • •Work across LLM/VLM and specialized deep learning domains (e.g., NLP, computer vision, multimodal).
  • •Apply strong software engineering practices such as version control, CI/CD, and maintaining code quality.

Key Requirements

  • •5-8 years of experience in deep learning.
  • •Strong foundational knowledge of deep learning concepts and architectures, including LLMs and VLMs.
  • •Expertise in at least one specialized area such as NLP, computer vision, or multimodal models.
  • •Experience building and deploying production-grade deep learning systems at scale.
  • •Familiarity with large language models and their applications (e.g., GPT, LLaMA, Claude).
Experience:5-8 years
Skills:Continuous learningExperimentationAbility to rapidly learnSoftware engineering practices
Tech Stack:LLMsVLMsNLPComputer visionMultimodal modelsGPTLLaMAClaudeAuto-MLCI/CDVersion control

Company Brief

NanoNets
Provides AI-based document automation and OCR solutions to extract data from invoices, receipts, forms, and documents using customizable machine learning models and APIs for enterprise workflows.
Industry: SaaS
Company Size: Medium (51 to 250 employees)
Growth: Growth Stage Startup
Headquarters: Boston, United States
WebsiteLinkedIn