Python ML Engineer AI/NLP Pipelines

3 - 6 years

20 - 32 Lacs

Posted:19 hours ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

data processing, ML inference pipelines, and API development

clean, scalable code

You will work on:

  • Text/data parsing
  • Classification pipelines
  • Model inference services (FastAPI)
  • Performance optimisation (batching, vectorisation)
  • Small ML/NLP models in production environments

Key Skills (Must Have)

  • Python (3+ years) production environment

  • FastAPI or Flask

  • pandas / numpy (data cleaning / preprocessing)

  • scikit-learn (basic ML)

  • Text processing / regex / NLP fundamentals

  • Docker & deployment basics

  • Model optimisation or inference experience (ONNX / batching / vectorisation)

Good to Have (Optional)

  • Hugging Face transformers (basic usage)
  • Experience with embeddings or multi-label classification
  • Exposure to ONNX Runtime / quantization
  • AWS / GCP deployment basics
  • Git, CI/CD, testing experience

Responsibilities

  • Build and maintain Python microservices for ML inference
  • Implement batching, caching, and performance improvements
  • Parse real-world product data and clean text fields
  • Integrate lightweight ML/NLP models into APIs
  • Work closely with international backend + data teams

What We Offer

  • Work on a global SaaS platform used by chefs worldwide
  • Modern tech: FastAPI, model optimisation, lightweight NLP
  • Opportunities to grow into ML Engineering or MLOps
  • Collaborative international culture

Why This Role is Exciting

  • Have you ever wanted to build ML pipelines using

    real-world messy data

    , not just Kaggle?
  • Would you enjoy owning a full flow:

    parse clean infer output

    ?
  • Interested in learning

    ONNX, quantisation, optimisation, and batch inference

    ?
  • Want to work on a

    global SaaS product

    used daily by chefs, dietitians, and foodservice operators?
  • Keen to modernise a

    20-year-old platform

    using Python, FastAPI, and practical AI?

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