ML Engineer

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Experience: 3+ yrs

Location: Remote (India)

Job Type: Full-time

We are looking for an experienced ML Engineer to design, build, deploy, and maintain advanced Machine Learning, Large Language Model (LLM), and multi-agent AI systems that improve healthcare operations and workflows.

The role combines machine learning engineering, generative AI, backend development, cloud infrastructure, and AI research. The ideal candidate will have strong hands-on experience building scalable ML systems, developing LLM-based solutions, and deploying production-grade AI services in secure and reliable cloud environments.

Key Responsibilities

• Design, develop, deploy, monitor, and maintain proprietary ML models, LLMs, and multi-agent AI systems for healthcare applications.
• Develop AI solutions that improve healthcare operations, workflows, efficiency, and service delivery.
• Customise and fine-tune open-source LLMs and integrate enterprise LLM platforms for healthcare-specific requirements.
• Develop effective prompting strategies to improve LLM performance across complex healthcare use cases.
• Build AI solutions for workflows such as prior authorisation and other healthcare operational processes.
• Develop scalable, secure, and maintainable Python microservices using FastAPI.
• Design and implement RESTful APIs and backend services supporting ML and AI applications.
• Deploy and orchestrate services using Kubernetes, with a strong focus on reliability, scalability, security, and operational performance.
• Work with GCP infrastructure to deploy and manage production AI and ML workloads.
• Monitor model and service performance and continuously optimise reliability, latency, scalability, and resource utilisation.
• Research emerging AI and ML techniques applicable to healthcare and translate relevant research into practical solutions.
• Conduct independent technical research and contribute to scientific publications and research papers.
• Develop intelligent simulation systems that emulate or automate service-led workflows to achieve efficiency and cost improvements.
• Collaborate with product, engineering, healthcare, and other stakeholders to translate requirements into effective AI solutions.
• Ensure AI systems follow appropriate ethical, privacy, security, and healthcare regulatory requirements.
• Maintain technical documentation and communicate AI concepts, system capabilities, limitations, and outcomes to technical and non-technical stakeholders.
• Contribute to continuous improvement of AI engineering practices, model development processes, and production infrastructure.

What Makes You a Great Fit

• 3–5 years of experience building scalable ML systems, AI applications, and backend services.
• Bachelor's degree in Computer Science, Engineering, or a related discipline, preferably from a Tier-I institution.
• Strong hands-on expertise in Large Language Models (LLMs), prompting, fine-tuning, and Generative AI.
• Strong understanding of machine learning concepts and practical experience with TensorFlow, PyTorch, or similar frameworks.
• Experience developing, deploying, monitoring, and optimising production ML models.
• Strong proficiency in Python and hands-on experience with FastAPI for building RESTful microservices.
• Experience with Kubernetes and containerised application deployment.
• Familiarity with Google Cloud Platform (GCP) and cloud-based ML/AI infrastructure.
• Experience building scalable backend systems and production-grade AI services.
• Ability to conduct independent AI/ML research and contribute to scientific papers or technical publications.
• Strong analytical and problem-solving skills with an ability to translate research into practical engineering solutions.
• Understanding of AI ethics, healthcare data privacy, security, and regulatory considerations.
• Excellent written and verbal communication skills, including the ability to explain complex technical concepts to non-technical stakeholders.
• Strong ownership, collaboration, and execution skills in fast-paced, cross-functional environments.
• Willingness to travel to the Vadodara, Gujarat headquarters for approximately one week when required.