Back
Tier-1 brand plus visible AI role increases competition, though seniority and niche LLMOps moderate applicant density.
LLMOps and advanced AI engineering skills transfer across industries, though enterprise compliance increases domain specificity.
Multiple mandatory ML/LLM skills, LLMOps experience, and senior leadership expectations create strict filters.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own design and implementation of scalable AI workflows using firm’s AI framework, including multi-step reasoning and agent-based solutions.
Lead delivery and maintenance of production-grade AI systems, including model deployment, APIs, orchestration pipelines, and enterprise integrations.
Define technology roadmaps and best practices for AI adoption; mentor junior engineers on advanced AI techniques.
Proficiency in Python and machine learning frameworks like TensorFlow and PyTorch; experience with data manipulation libraries (Pandas, NumPy).
Experience in spec-driven engineering (SDD), structured problem decomposition, and translating business needs into AI workflows and deployable architectures.
Hands-on experience with Model Context Protocol (MCP), AI orchestration layers, and developing LLM-based Retrieval Augmented Generation (RAG) solutions.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in building enterprise-grade AI systems focused on performance, scalability, security, and responsible AI.
Strong ability to translate complex business challenges into AI use cases and lead proof-of-concepts with clear production paths.
Clear communicator able to explain complex AI concepts to both technical and non-technical stakeholders through demos and prototypes.