Senior LLM Engineer

3 days ago


Greater Hyderabad Area, India Altysys Full time ₹ 15,00,000 - ₹ 20,00,000 per year

Experience : 7+ Years

Relevant Experience : 4+ Years

Work Mode : Gurgaon

Budget : 2.6lpm

Key Responsibilities:

  • Model Expertise:
    Work with transformer models (GPT, BERT, T5, RoBERTa, etc.) across NLP tasks including text generation, summarization, classification, and translation.
  • Model Fine-Tuning:
    Fine-tune pre-trained models on domain-specific datasets to optimize for summarization, text generation, question answering, and related tasks.
  • Prompt Engineering:
    Design, test, and iterate on contextually relevant prompts to guide model outputs for desired performance.
  • Instruction-Based Prompting:
    Implement and refine instruction-based prompting strategies to achieve contextually accurate results.
  • Learning Approaches:
    Apply zero-shot, few-shot, and many-shot learning methods to maximize model performance without extensive retraining.
  • Reasoning Enhancement:
    Leverage Chain-of-Thought (CoT) prompting for structured, step-by-step reasoning in complex tasks.
  • Model Evaluation:
    Evaluate model performance using BLEU, ROUGE, and other relevant metrics; identify opportunities for improvement.
  • Deployment:
    Deploy trained and fine-tuned models into production environments, integrating with real-time systems and pipelines.
  • Bias & Reliability:
    Identify, monitor, and mitigate issues related to bias, hallucinations, and knowledge cutoffs in LLMs.
  • Collaboration:
    Work closely with cross-functional teams (data scientists, engineers, product managers) to design scalable and efficient NLP-driven solutions.

Must-Have Skills:

  • 7+ years of overall experience in software/AI development with
    at least 2+ years in transformer-based NLP models
    .
  • 4+ years of hands-on expertise with
    transformer architectures
    (GPT, BERT, T5, RoBERTa, etc.).
  • Strong understanding of
    attention mechanisms, self-attention layers, tokenization, embeddings, and context windows
    .
  • Proven experience in
    fine-tuning pre-trained models
    for NLP tasks (summarization, classification, text generation, translation, Q&A).
  • Expertise in
    prompt engineering
    , including zero-shot, few-shot, many-shot learning, and prompt template creation.
  • Experience with
    instruction-based prompting
    and
    Chain-of-Thought prompting
    for reasoning tasks.
  • Proficiency in
    Python
    and NLP libraries/frameworks such as
    Hugging Face Transformers, SpaCy, NLTK, PyTorch, TensorFlow
    .
  • Strong knowledge of
    model evaluation metrics
    (BLEU, ROUGE, perplexity, etc.).
  • Experience in deploying models into
    production environments
    .
  • Awareness of
    bias, hallucinations, and limitations in LLM outputs
    .

Good to Have:

  • Experience with
    LLM observability tools
    and monitoring pipelines.
  • Exposure to
    cloud platforms
    (AWS, GCP, Azure) for scalable model deployment.
  • Knowledge of
    MLOps practices
    for model lifecycle management.

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