{"id":3776387,"url":"https://alion.io/job/celebal-databricks-genai-solutions-architect","title":"Databricks GenAI Solutions Architect","company":{"id":4442899,"name":"Celebal","domain":"celebal.com","url":"https://alion.io/company/celebal","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Zoho Recruit","truth_index":null},"role":"Solutions","role_family":"Solutions","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"Azure","optional":false},{"name":"Chain-of-Thought","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"FAISS","optional":false},{"name":"Few-Shot Learning","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"Llama","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"LoRA","optional":false},{"name":"Mistral","optional":false},{"name":"MLFlow","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"PEFT","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"QLoRA","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Vertex AI","optional":false},{"name":"Weaviate","optional":false},{"name":"Machine Learning","optional":true},{"name":"Transformers","optional":true}],"status":"live","first_seen_at":"2026-11-04T17:24:12Z","employer_posted_date":null,"last_verified_at":"2026-11-04T17:24:12Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"About the Company\n\nCelebal Technologies is a premier software services company in the field of Data Science, Big Data and Enterprise Cloud. Celebal Technologies helps you to discover the competitive advantage by employing intelligent data solutions using cutting-edge technology solutions that can bring massive value to your organization. The core offerings are around Data to Intelligence, wherein we leverage data to extract intelligence and patterns thereby facilitating smarter and quicker decision making for clients.\n\nCelebal Technologies solutions are powered by Robotics, Artificial Intelligence and Machine Learning algorithms which offer improved business efficiency in the interconnected world. Companies clients are Product ISVs and delivery organisations across the globe who use our niche expertise in product development as well as Enterprise project implementations. We have adopted the highest standards of service quality and operational excellence, enabling its clients across a wide range of industries to transform into a data-driven enterprise. Our tailor-made solutions help enterprises maximise productivity, improve speed and accuracy.\n\nAbout the Role\n\nWe 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.\n\nResponsibilities\n\nGenAI Architecture & Development\nArchitect end-to-end GenAI solutions including RAG (Retrieval-Augmented Generation), AI agents, and fine-tuned models on Databricks\nEvaluate and select appropriate LLMs (OpenAI, Anthropic Claude, Azure OpenAI, DBRX, Llama, Mistral) based on use case requirements and cost optimization\nDesign and implement scalable vector search solutions using Databricks Vector Search and Mosaic AI\nEstablish prompt engineering best practices, optimization techniques, and prompt management strategies\nImplement compound AI systems and multi-agent architectures using Databricks Agent Framework\nDatabricks Platform Expertise\nDesign and implement GenAI solutions leveraging Databricks Mosaic AI capabilities including Model Serving, Foundation Model APIs, Vector Search, AI Gateway, and Feature & Function Serving\nArchitect data pipelines using Delta Lake and Spark for preprocessing, chunking, and embedding generation at scale\nImplement robust data governance using Unity Catalog for model assets, features, and vector indices\nDesign and manage MLflow experiments for LLM evaluation, tracking, and model registry\nLLMOps & Production Deployment\nDesign LLMOps pipelines for evaluation, monitoring, A/B testing, and continuous improvement\nImplement guardrails, safety measures, content filtering, and responsible AI practices\nBuild observability solutions for LLM applications including latency monitoring, cost tracking, and quality metrics\nEstablish CI/CD pipelines for GenAI model deployment using Databricks Workflows and Asset Bundles\nLeadership & Collaboration\nLead technical decisions and conduct architecture reviews for GenAI initiatives\nCollaborate with business stakeholders to translate requirements into scalable technical solutions\nMentor engineers and data scientists on GenAI development standards and Databricks best practices\nCreate technical documentation, reference architectures, and reusable solution accelerators\n\nQualifications\n\nGenAI/LLM Expertise\n5+ years of relevant experience in ML/AI with 3+ years focused on GenAI/LLM applications\nDeep hands-on experience with LLM APIs (OpenAI, Anthropic Claude, Azure OpenAI, Google Vertex AI)\nStrong RAG implementation experience including document processing, chunking strategies, embedding models, and retrieval strategies (hybrid search, re-ranking, query transformation)\nExperience with vector databases (Pinecone, Weaviate, ChromaDB, FAISS, Azure AI Search, Databricks Vector Search)\nAdvanced prompt engineering skills including chain-of-thought, few-shot learning, and prompt optimization\nProficiency with LLM orchestration frameworks (LangChain, Langgraph, Semantic Kernel)\nUnderstanding of fine-tuning techniques (LoRA, QLoRA, PEFT, full fine-tuning)\nDatabricks Platform Skills\n3+ years hands-on experience with Databricks platform\nStrong proficiency in PySpark and Spark SQL for large-scale data processing\nExperience with Databricks Mosaic AI suite: Model Serving, Vector Search, Foundation Model APIs, AI Gateway\nExpertise in MLflow for experiment tracking, model registry, and deployment\nWorking knowledge of Unity Catalog for data and AI governance\nExperience with Delta Lake architecture and optimization\nFamiliarity with Databricks Workflows for orchestration\nTechnical Foundations\nStrong programming skills in Python; familiarity with SQL\nProduction deployment experience for LLM applications at scale\nExcellent system design and distributed architecture skills\nUnderstanding of API design, microservices, and cloud-native patterns\nExperience with containerization (Docker, Kubernetes) for model serving\n\nRequired Skills\n\nGenAI/LLM Expertise\nDeep hands-on experience with LLM APIs (OpenAI, Anthropic Claude, Azure OpenAI, Google Vertex AI)\nStrong RAG implementation experience including document processing, chunking strategies, embedding models, and retrieval strategies (hybrid search, re-ranking, query transformation)\nExperience with vector databases (Pinecone, Weaviate, ChromaDB, FAISS, Azure AI Search, Databricks Vector Search)\nAdvanced prompt engineering skills including chain-of-thought, few-shot learning, and prompt optimization\nProficiency with LLM orchestration frameworks (LangChain, Langgraph, Semantic Kernel)\nUnderstanding of fine-tuning techniques (LoRA, QLoRA, PEFT, full fine-tuning)\nDatabricks Platform Skills\n3+ years hands-on experience with Databricks platform\nStrong proficiency in PySpark and Spark SQL for large-scale data processing\nExperience with Databricks Mosaic AI suite: Model Serving, Vector Search, Foundation Model APIs, AI Gateway\nExpertise in MLflow for experiment tracking, model registry, and deployment\nWorking knowledge of Unity Catalog for data and AI governance\nExperience with Delta Lake architecture and optimization\n\nAny Location- Jaipur/ Noida/Gurgaon/ Pune/ Bangalore/ Hyderabad","description_format":"text","description_chars":6647,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["AI Consulting & Integration","Analytics & BI Consulting","Data Engineering & Migration Services"],"lifecycle":[{"event":"open","at":"2026-11-04T23:09:45Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring 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