LLM Engineer

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:

• Bachelor's 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.