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LLM Engineer

8 - 13 years

7 - 11 Lacs

Posted:1 week ago| Platform: Naukri logo

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Full Time

Job Description

Years of Experience: 8+ Years Work Mode: Hybrid Overview: Join our team to drive enterprise LLM initiatives, combining expert-level LLM knowledge with strong security and data integration expertise. You will oversee the architecture, development, and deployment of advanced ML solutions that integrate Microsoft Teams, Copilot 365, and Azure data tools delivering secure and scalable outcomes across the organization. Key Responsibilities: Lead architectural design and development for enterprise LLM solutions, emphasizing expert-level proficiency with LLM Agents (highest priority). Ensure robust data security, leveraging strong knowledge of enterprise security practices. Configure and maintain both vector and structured databases, with solid hands-on experience in ETL pipelines. Oversee essential guardrails for LLMs, including basic observability, semantic layers, and model distillation for performance tuning. Coordinate role-based access controls (RBAC) and manage comprehensive data governance frameworks. Communicate complex technical strategies to executives, creating clear diagrams and abstractions. Guide cross-functional teams, balancing moderate risk-taking with innovative solution delivery. Required Skills Experience: Thorough expertise in LLM development and deployment (expert-level). Significant experience applying enterprise security best practices (strong knowledge). Proficiency in vector databases, structured datastores, and ETL workflows (solid experience). Familiarity with LLM observability, guardrails, semantic layers, and model distillation (basic working knowledge). Strong leadership and communication skills, with the ability to present to executives and abstract complex ideas. Comfortable driving projects independently, managing risk, and fostering collaboration. Preferred: Experience integrating Microsoft Teams, Copilot 365, Azure Data Factory/Synapse, and Dynamics 365/Dataverse. Demonstrated success in designing and implementing large-scale ML solutions in enterprise environments.

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