Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Tier‑2 brand, hybrid role, popular ML title, and broad skillset requirements drive high competition.
Core ML/AI skills transfer across industries, though healthcare preference raises domain specificity moderately.
Explicit 6+ years plus mandatory LLM, RAG, vector DB, cloud and MLOps skills create high filtering strictness.
Design, develop, deploy, and optimize AI/ML and Generative AI solutions including LLMs, NLP, deep learning, RAG architectures, and vector databases in production environments.
Establish and manage MLOps practices for model deployment, monitoring, lifecycle management, and ensure solutions meet security, compliance, scalability, and performance standards.
Collaborate with cross-functional teams and mentor junior AI engineers while driving AI best practices and innovation initiatives.
6-10+ years of relevant experience in AI/ML engineering or related roles.
Strong proficiency in Python programming and hands-on experience with Generative AI, Large Language Models (LLMs), TensorFlow, PyTorch, Scikit-learn, LangChain, or LlamaIndex.
Experience with MLOps, CI/CD pipelines, model deployment, and monitoring of production-scale AI systems.
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
Experienced in implementing Retrival-Augmented Generation (RAG) architectures and working with vector databases such as Pinecone, FAISS, Chroma, or Azure AI Search.
Hands-on experience with cloud AI platforms like Azure AI, AWS AI/ML, or Google Cloud AI and familiarity with REST APIs, microservices, Docker, and Kubernetes.
Background or preference for healthcare domain and knowledge of AI governance, responsible AI practices and enterprise AI solution delivery.