LLM Engineer

4 days ago

Bengaluru, India Net Connect Full time

Notice Period: Immediate to 15 Days

Role Overview:

We are seeking a skilled and innovative LLM Engineer to design, implement, and optimize advanced GenAI solutions across drug discovery, clinical development, and manufacturing. The ideal candidate will have hands-on experience with LLMs, deep learning, MLOps, and agentic frameworks, with a strong focus on building scalable AI infrastructure and production-ready ML pipelines.

Key Responsibilities:

  • Design and implement LLM training and fine-tuning pipelines (e.g., using DeepSpeed, PEFT, RLHF).
  • Collaborate with data scientists to customize and deploy LLMs in use cases such as target discovery, clinical trial optimization, and molecule design.
  • Build scalable, reusable backend systems and GenAI agentic frameworks (AutoGen, LangGraph) to power R&D applications.
  • Develop and maintain robust evaluation frameworks, telemetry systems, and performance monitoring tools.
  • Operationalize models on Azure and GCP with MLOps best practices, working closely with the DevSecOps team.
  • Design data pipelines to support rapid ML experimentation and model lifecycle management.
  • Evaluate and integrate third-party GenAI tools and co-develop solutions with external technology partners.

Required Qualifications:

  • Bachelors degree in Computer Science, Engineering, or a related quantitative field.
  • 3-5 years of experience in ML/AI engineering, with a strong focus on LLMs and GenAI.
  • Proficiency in Python and frameworks such as PyTorch or TensorFlow.
  • Experience with LLM technologies, including embedding techniques, RAG, fine-tuning, pre-training, and evaluation benchmarks.
  • Hands-on experience with agentic AI frameworks like AutoGen and LangGraph.
  • Solid experience in MLOps, model deployment, and cloud infrastructure (Azure, GCP).
  • Full-stack engineering mindset with familiarity in CI/CD, testing, code reviews, and scalable software development.
  • Strong problem-solving abilities and communication skills.

Preferred:

  • Experience in life sciences, healthcare, or pharma R&D applications is a plus.
  • Familiarity with data governance, model interpretability, and responsible AI practices.

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