Match Score
Against your primary resumeLogin to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Protocol Intelligence
Data-driven signals on your job's competitivenessMetro location plus broad AI/ML and Azure skillset increases applicant density, though seniority narrows the pool.
Specialized Azure AI and enterprise MLOps experience moderately limits cross-industry transferability.
Explicit 8+ years plus seniority and deep Azure, LLM, and MLOps requirements create strict filtering.
Job Description
Structured overview of role & requirementsAbout This Role
Lead design and implementation of secure, scalable Azure-native AI systems including ML, generative AI, multimodal AI, and agent-based automation.
Own model development lifecycle, pipeline optimization, deployment, and contribute to enterprise MLOps and LLMOps best practices.
Define and govern enterprise AI architecture roadmap aligned to Microsoft Azure ecosystem and ensure compliance with security, privacy, and responsible AI principles.
Minimum Requirements
8+ years of experience in AI/ML, data platforms, or advanced analytics with minimum 3+ years in Senior/Principal Engineer roles.
Expertise in Microsoft Azure AI & data services including Azure AI Studio, Azure OpenAI, Azure Machine Learning, Azure Data Lake, Synapse, AKS, and GPU-backed compute.
Strong hands-on experience with Large Language Models (LLMs), embeddings, fine-tuning, multimodal AI, Retrieval-Augmented Generation (RAG), vector search, and semantic retrieval.
Bachelor’s degree in Computer Science, Engineering, or related field.
Ideal Candidate Profile
Experienced in architecting enterprise-scale AI platforms and pipelines specifically within Microsoft Azure ecosystem.
Demonstrated capability in operationalizing MLOps and LLMOps practices at scale including CI/CD, monitoring, and governance.
Skilled in translating business/domain needs into scalable, compliant AI architectures and acting as a technical thought leader advising senior leadership.