Overview

Key Responsibilities

Responsibilities

• Design, build, and optimize speech-to-text (STT) pipelines for product features.
• Fine-tune, evaluate, and deploy open-source LLMs and other ML models.
• Design and build recommendation engines for content relevance and engagement.
• Work across the full ML lifecycle: data collection, curation, training, evaluation, deployment, and monitoring.
• Collaborate with backend engineers to integrate models into production APIs and services.
• Evaluate new open-source models, techniques, and tools for STT, recommendations, and LLM fine-tuning.
• Optimize models and pipelines for latency, cost, and accuracy.
• Build evaluation frameworks and benchmarks to measure model quality and identify regressions.
• Work with vector databases, retrieval pipelines, and search infrastructure.
• Investigate and resolve production issues related to model performance, latency, and output quality.
• Document experiments, model decisions, and architecture choices.

Qualifications

• 3+ years of hands-on experience building and deploying ML/AI systems in production.
• Pharma / Healthcare / Medical project experience preferred
• Experience with STT systems, including building, fine-tuning, or integration.
• Experience training or fine-tuning open-source models, including LLMs or other architectures.
• Experience building recommendation systems or ranking/personalization models.
• Solid understanding of the ML lifecycle: data pipelines, training, evaluation, deployment, and monitoring.
• Solid experience with Python and standard ML/DL frameworks such as PyTorch and Hugging Face.
• Ability to independently investigate unfamiliar technical problems and identify practical solutions.
• Strong sense of ownership and ability to take models from idea to production.
• Genuine interest in AI research, new models, and experimentation. .