LLM Engineer

Role Overview:

LLM Engineer

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:

• Bachelor's degree in Computer Science, Engineering, or a related quantitative field.

• 35 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.