Machine Learning Engineer

Remotestar; Bengaluru; On-site; INR 600,000 - 1,200,000; Full time

Job summary

Remotestar is hiring a Machine Learning Engineer in Bengaluru to work on AI-driven revenue cycle management solutions. This role will involve developing and maintaining ML models for Natural Language Processing tasks, implementing advanced AI techniques, and collaborating on innovative solutions.

The ideal candidate will possess strong programming skills in Python, experience with deep learning frameworks, and the ability to contribute to both standalone projects and team initiatives. A role in a transformative environment awaits!

Qualifications

• 2-4 years of experience in machine learning or applied AI roles.
• Experience in hosting and maintaining ML models at scale.
• Strong communication skills to convey complex ideas.

Responsibilities

• Develop, deploy, and maintain ML models for NLP tasks.
• Design and set up self-hosted models for efficiency.
• Implement prompt refinement techniques to improve results.

Skills

Natural Language Processing (NLP)

Python programming

Deep Learning frameworks (PyTorch, TensorFlow)

Machine Learning principles

Statistical techniques

Education

Bachelor’s degree in Computer Science, Machine Learning, or a related field

Tools

Docker

Kubernetes

Job description

Location: Bengaluru (Onsite)

Years of Experience: 2 to 4

At RemoteStar, we are hiring for one of our clients - a leader in AI-driven revenue cycle management, transforming medical coding and revenue operations with cutting-edge technology.

The company has raised $11 million in total funding from top investors, including Accel and Together Fund.

Join us as we scale a cloud-native platform that runs transformer-based Large Language Models rigorously fine-tuned on millions of clinical notes and claims every month. You’ll engineer autonomous coding pipelines that parse ICD-10-CM, CPT® and HCPCS at lightning speed, deliver reimbursement insights with sub-second latency and >99 % accuracy, and tackle the deep-domain challenges that make clinical AI one of the hardest—and most rewarding—problems in tech.

Machine Learning Engineer - Job Overview

We are seeking a skilled and innovative Machine Learning Engineer with expertise in Natural Language Processing (NLP) and an interest in the latest AI models. This role focuses on driving innovation by introducing non-traditional approaches, deploying scalable models, and implementing advanced AI techniques to solve real-world challenges. The ideal candidate is a strong programmer with a research mindset and the ability to translate the latest advancements in AI into impactful solutions.

Key Responsibilities

• Develop, deploy, and maintain ML models and pipelines for NLP tasks.
• Design and set up self-hosted models for specific tasks, ensuring efficiency and scalability.
• Establish workflows for fine-tuning, feedback loops, and online learning to enhance model performance.
• Implement and optimise prompt refinement techniques to improve results.
• Continuously research and evaluate state-of-the-art AI advancements to integrate into the organisation's solutions.
• Contribute to existing and new projects by:
  • Streamlining processes for better estimation and scaling solutions.
  • Enhancing testing tools and pipelines for AI development.
  • Collaborating with teams to identify opportunities for applying AI and ML to solve complex problems efficiently.
  • Building and productionising deep learning models to ensure seamless integration into existing systems.

Requirements

• Bachelor’s degree in Computer Science, Machine Learning, or a related field.
• 2-4 years of experience in machine learning or applied AI roles.
• Strong Python skills with experience in hosting and maintaining ML models at scale.
• Proficiency in a deep learning framework (e.g., PyTorch or TensorFlow).
• Ability to explore and evaluate research papers, identifying key methods and techniques for application.
• Capability to combine existing tools and frameworks to create efficient and effective solutions.
• Solid foundation in machine learning principles and statistical techniques.
• Strong communication skills to convey complex ideas to technical and non-technical stakeholders.

Preferred Qualifications

• Familiarity with cloud platforms, containerization tools (e.g., Docker, Kubernetes), and production-level AI workflows.
• Experience working with domain-specific data, such as clinical or biomedical datasets.
• Interest in cutting-edge AI techniques, with aspirations to co-author research or contribute to industry advancements.
• Knowledge of advanced NLP techniques, including prompt engineering and refinement frameworks.
• Exposure to signal processing applications in various domains.