senior NLP Engineer

5 days ago

Hyderabad, Telangana, India PROADSW3 Relations pvt. ltd. Full time ₹ 18,00,000 - ₹ 22,00,000 per year


Senior NLP Engineer (Transformer Models)


Location:
Hyderabad |
Budget:
₹1.8 – ₹2.2 LPM


Experience:
7+ Years (2+ Years in Transformer-based NLP Models)

About the Role

We are looking for an experienced
Senior NLP Engineer
with strong expertise in
transformer-based language models
(GPT, BERT, T5, RoBERTa, etc.) to join our AI team. You will play a key role in fine-tuning large language models, building production-ready NLP systems, and driving innovation in text understanding and generation tasks.

Key Responsibilities

  • Work extensively with
    transformer models
    (GPT, BERT, T5, RoBERTa, etc.) across a wide range of NLP tasks.
  • Fine-tune pre-trained models
    for summarization, text classification, question answering, and text generation.
  • Design, refine, and optimize
    prompts
    (zero-shot, few-shot, many-shot, and chain-of-thought).
  • Deploy
    fine-tuned and customized models into production environments with scalability and performance in mind.
  • Evaluate model performance using standard metrics such as
    BLEU, ROUGE, perplexity
    , and more.
  • Collaborate closely with
    cross-functional teams
    including data scientists, ML engineers, and software developers to deliver AI-driven solutions.

Must-Have Skills

  • 7+ years
    of experience in AI/software development, including
    4+ years
    with transformer-based NLP models.
  • Proficiency in
    Python
    ,
    Hugging Face
    , and
    PyTorch/TensorFlow
    frameworks.
  • Strong understanding of
    attention mechanisms
    ,
    embeddings
    , and
    tokenization techniques
    .
  • Hands-on experience with
    prompt engineering
    and
    reasoning-focused NLP tasks
    .
  • Proven experience in
    model deployment and production workflows
    .

Good to Have

  • Experience with
    cloud platforms
    (AWS, GCP, Azure).
  • Familiarity with
    LLM observability tools
    and
    MLOps practices
    .
  • Exposure to large-scale
    inference optimization
    and
    model monitoring
    .

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