Senior AI / ML Engineer - Databricks

Description : Description : Senior Engineer - AI / ML (Databricks are seeking a highly skilled AI Engineer with proven experience in developing, deploying, and optimizing AI models using the Databricks AI platform. The ideal candidate will have a strong background in machine learning, distributed data processing, and will be a champion of Databricks :

• Generative AI : Experience of LLM-based solutions (LlamaIndex, LangChain, RAG pipelines, or similar frameworks). Ability to integrate GenAI and Agentic AI into business workflows.
• Design and implement end-to-end Generative AI solutions on Databricks, leveraging Unity Catalog, MLflow, Delta Lake, and Vector Search
• Design, build, and deploy large-scale AI / ML models using the Databricks environment.
• Implement data validation, lineage, and monitoring using Delta Live Tables and Unity Catalog.
• Leverage Databricks data engineering workflows for feature engineering, model training, and evaluation.
• Optimize training pipelines for efficiency, scalability, and accuracy.
• Integrate AI models into production systems using APIs and microservices.
• Build reusable ML pipelines using Databricks Repos, MLflow, and Feature Store.
• Implement robust testing, monitoring, and retraining protocols for deployed models.
• Ensure adherence to compliance, security, and performance standards.
• Stay updated on advancements in AI frameworks, distributed computing, and Databricks platform Skills and Qualifications :
• Bachelors or Masters degree in Computer Science, Data Science, or related field.
• Cerified Databricks Certified Generative AI Engineer
• Proven experience developing AI solutions on Databricks.
• Strong knowledge of Python, PySpark, MLflow, Spark and Databricks Notebooks.
• Strong understanding and knowlege of Databricks platform features such as Unity Catalog, DLT, MosaicAI, Data Assets Bundles, etc.
• Experience with Transformer-based models, generative AI, and Databricks pipelines.
• Proficiency in integrating AI models with cloud-native architectures (AWS, Azure, or GCP).
• Solid understanding of MLOps practices, Data Assets bundles (CI / CD), and containerization (Docker, Kubernetes) on Databricks Familiarity with vector databases, embeddings, and retrieval-augmented generation (RAG).
• Strong problem-solving, analytical thinking, and communication have Skills :
• Databricks - 5 Years - Advanced
• Python - 8 Years
• Generative AI - 8 Years - Advanced
• Unity catalogue - 4 Years - Advanced
• ML flow - 4 Years - Advanced
• Delta lake - 5 Years - Advanced
• Vector search - 4 Years - Advanced
• PySpark - 5 Years - Advanced

(ref : hirist.tech)