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Niche LLM/MLOps skillset reduces competition, but Bangalore metro and mid-level seniority raise applicant density.
Requires specialized LLM and MLOps expertise that is technically transferable but sensitive to domain-specific healthcare experience.
High due to explicit 6+ years requirement and many mandatory LLM, MLOps, cloud, and Kubernetes skills.
Develop and maintain microservice architectures and APIs for AI solution deployment, utilizing technologies like Python, FastAPI, and Kubernetes.
Design and implement scalable data pipelines and LLM inference architectures, optimizing model performance and deployment including GPU memory management and quantization.
Collaborate cross-functionally with data scientists and product managers to integrate, optimize, and deploy AI/ML models, applying MLOps practices and infrastructure management tools such as Terraform and CI/CD platforms.
Bachelor’s degree in any Engineering stream; Computer Science/Engineering preferred but not mandatory.
Minimum 6+ years in AI Engineering.
Proficiency in Python with expertise in machine learning frameworks (PyTorch, TensorFlow), experience in microservices architecture, and Kubernetes container orchestration.
Experience with microservices, REST/gRPC APIs, LLM frameworks (Hugging Face Transformers, LangChain), vector databases (Qdrant, Chromadb), and infrastructure as code tools (Terraform, CloudFormation).
Experienced specialist in AI Engineering with a strong background in large language model deployment and optimization, including prompt engineering, quantization, and knowledge distillation.
Capable of managing end-to-end AI/ML deployment lifecycle in cloud and containerized environments with operational skills on CI/CD, Kubernetes, and infrastructure automation.
Proven ability to collaborate with cross-functional teams for delivering advanced AI solutions in production, with experience in healthcare domain AI considered advantageous though not mandatory.