Machine Learning Engineer (LLM)

Role Description As a Machine Learning Engineer specializing in Large Language Models (LLMs) at Medicoder, you will operate at the intersection of state-of-the-art AI engineering and critical, real-world clinical deployment.

You will own the end-to-end lifecycle of LLMs and deploy optimized, privacy-first solutions directly into production within local and international hospitals. You will work with a diverse data ecosystem, including public benchmarks, proprietary datasets, and highly sensitive clinical data.

Because our systems interface directly with critical hospital infrastructure, you will balance maximizing raw model capability with the strict requirements of data privacy, low-latency performance, and product reliability.

Key Responsibilities

Collaborate closely with the product team to deeply understand user requirements, customer pain points, and product vision, translating them into robust, actionable technical specifications for model development.

Rigorously evaluate and benchmark State-of-the-Art (SOTA) LLMs (both proprietary closed-source and open-source models) against complex, domain-specific medical and administrative tasks to ensure maximum product reliability.

Fine-tune existing architectures and train domain-specific models from scratch using public, proprietary, and unstructured clinical datasets to directly improve product features.

Translate AI capabilities into user value by deploying, monitoring, and maintaining high-performance LLM pipelines directly within local hospital environments and clinical workflows.

Optimize models for real-world constraints, focusing on reducing latency, managing GPU memory footprint, and lowering inference costs so our customers experience a seamless, lightning-fast product.

Curate, synthesize, and clean high-quality pre-training and fine-tuning datasets from complex, multi-modal medical records to continuously fuel product iterations. Required Qualifications & Skills Bachelor's or Master's degree or above in CS,