Job Opportunities API

Job Description We are looking for an experienced  AI Architect  with strong expertise in  Databricks, Data Engineering, Machine Learning, and Cloud-based AI solutions  to lead the design and implementation of scalable AI/ML platforms and intelligent data solutions. The ideal candidate will define enterprise AI architecture, drive modern data and AI transformation initiatives, and work closely with business, engineering, and analytics teams to deliver high-impact solutions. This role requires a strong understanding of  end-to-end AI/ML lifecycle ,  Lakehouse architecture ,  Databricks ecosystem , and the ability to translate business use cases into production-ready AI solutions. Key Responsibilities Design and implement  enterprise AI/ML architecture  using  Databricks Lakehouse Platform . Define scalable solutions for  data ingestion, feature engineering, model training, deployment, monitoring, and governance . Architect and optimize  AI/ML pipelines  using  Databricks, Spark, Python, SQL, and cloud-native services . Lead the setup of  MLOps / LLMOps frameworks  for model lifecycle management, CI/CD, model registry, and automated deployment. Work with business stakeholders to identify and prioritize  AI/ML use cases , including predictive analytics, NLP, recommendation engines, and generative AI. Build and guide architecture for  LLM / Generative AI solutions , including  RAG, vector databases, prompt orchestration, and model integration  where applicable. Establish best practices for  data quality, security, compliance, observability, scalability, and responsible AI . Collaborate with Data Engineers, Data Scientists, Product Owners, and Cloud teams to ensure solution alignment with enterprise architecture standards. Provide technical leadership in selecting AI/ML tools, frameworks, and cloud services aligned to business and platform strategy. Support architecture reviews, technical design workshops, PoCs, and enterprise AI roadmap planning. Required Skills & Experience 8- 15 years  of experience in  Data / AI / Analytics architecture , with strong exposure to enterprise-scale implementations. Hands-on experience with  Databricks  including: Databricks Lakehouse Delta Lake Unity Catalog MLflow Databricks Workflows Model Serving / Feature Store  (preferred) Strong programming experience in  Python, PySpark, SQL , and AI/ML solution development. Solid experience in designing and deploying  Machine Learning pipelines  in production. Good understanding of  MLOps / LLMOps , including model versioning, CI/CD, deployment, monitoring, and governance. Experience with  cloud platforms  such as  Azure, AWS, or GCP , preferably with Databricks integration. Familiarity with  Generative AI / LLM ecosystems , such as  OpenAI, Hugging Face, LangChain, vector stores, embeddings, and RAG architectures . Strong understanding of  data engineering, ETL/ELT, distributed computing, and data platform modernization . Experience in  solution architecture, technical governance, and stakeholder engagement .