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Tier-1 brand and metro locations increase candidate competition despite niche LLM/MLOps specialization.
Core LLM and MLOps skills are transferable, but healthcare domain knowledge and microservices make fit moderately sensitive.
Explicit 5+ years plus many mandatory LLM, MLOps, and infrastructure technologies makes shortlisting strict.
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Develop and maintain microservices architecture and API management solutions for AI deployments focused on large language models (LLMs).
Collaborate with cross-functional teams to design scalable data pipelines and deploy optimized AI/ML models using Kubernetes and container orchestration.
Lead advanced LLM optimization tasks including prompt engineering, model quantization, and inference architecture design to improve performance and efficiency in healthcare applications.
Bachelor’s degree in any Engineering stream (Computer Science/Engineering preferred but not mandatory).
Minimum 5+ years of relevant experience in AI Engineering, with a total preferred experience of 8 years.
Strong proficiency in Python, microservices architecture, LLM frameworks (Hugging Face, LangChain), Kubernetes, CI/CD, and cloud platforms (AWS/GCP/Azure).
Experience with AI/ML infrastructure tools like Terraform, CloudFormation, MLflow, vector databases, and LLM deployment techniques.
Experienced in building and deploying enterprise-grade AI microservices and LLM-driven applications in production environments using container orchestration and DevOps pipelines.
Demonstrated ability to optimize large language models through advanced techniques like quantization, knowledge distillation, and prompt engineering for scalable inference.
Domain familiarity with healthcare AI solutions, statistical methods for A/B testing, and integrating ML models into operational workflows.