skills:["Databricks","Mosaic AI","LLMs","MLflow","Prompt Engineering","Lakehouse Architecture"] , responsibilities:[" GenAI Architecture & Development

- Architect end-to-end GenAI solutions including RAG (Retrieval-Augmented Generation), AI agents, and fine-tuned models on Databricks.
- Evaluate and select appropriate LLMs (OpenAI, Anthropic Claude, Azure OpenAI, DBRX, Llama, Mistral) based on use case requirements and cost optimization.
- Design and implement scalable vector search solutions using Databricks Vector Search and Mosaic AI.
- Establish prompt engineering best practices, optimization techniques, and prompt management strategies.
- Implement compound AI systems and multi-agent architectures using Databricks Agent Framework.

"," Databricks Platform Expertise

- Design and implement GenAI solutions leveraging Databricks Mosaic AI capabilities including Model Serving, Foundation Model APIs, Vector Search, AI Gateway, and Feature & Function Serving.
- Architect data pipelines using Delta Lake and Spark for preprocessing, chunking, and embedding generation at scale.
- Implement robust data governance using Unity Catalog for model assets, features, and vector indices.
- Design and manage MLflow experiments for LLM evaluation, tracking, and model registry.

"," LLMOps & Production Deployment

- Design LLMOps pipelines for evaluation, monitoring, A/B testing, and continuous improvement.
- Implement guardrails, safety measures, content filtering, and responsible AI practices.
- Build observability solutions for LLM applications including latency monitoring, cost tracking, and quality metrics.
- Establish CI/CD pipelines for GenAI model deployment using Databricks Workflows and Asset Bundles.

"," Leadership & Collaboration

- Lead technical decisions and conduct architecture reviews for GenAI initiatives.




- Collaborate with business stakeholders to translate requirements into scalable technical solutions.
- Mentor engineers and data scientists on GenAI development standards and Databricks best practices.
- Create technical documentation, reference architectures, and reusable solution accelerators.

"],qualifications:["B.Tech/BCA or any Graduation", Advanced GenAI Capabilities

- Experience with open-source LLMs (Llama, Mistral, Mixtral, Phi)
- Knowledge of LLM evaluation frameworks (RAGAS, DeepEval, custom evaluation pipelines)
- Experience with multi-modal models (GPT-4o)
- Background in agentic AI, tool use, and function calling
- Experience with knowledge graphs and graph-based RAG

, Platform & Cloud

- Databricks Certified Data Engineer or ML Qualified certification
- Cloud architecture experience (Azure preferred; AWS/GCP acceptable)
- Experience with Azure OpenAI Service and Azure AI Foundry

],workMode:"Work from Office",description:"We are seeking a highly skilled Databricks GenAI Solutions Architect to lead the design, development, and deployment of enterprise-grade Generative AI solutions on the Databricks Lakehouse Platform. This role combines deep expertise in Large Language Models (LLMs) with comprehensive knowledge of the Databricks ecosystem, including Mosaic AI, MLflow, Unity Catalog, and Delta Lake. You will be responsible for architecting scalable LLM-based applications, implementing RAG pipelines, establishing MLOps/LLMOps best practices, and mentoring the team on cutting-edge GenAI technologies within the Databricks environment. " Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Gen AI Databricks Architect with Mosaic AI (Bengaluru)
🏢 Celebal Technologies
📍 Bengaluru