Role overview
Job Description: AI Engineer with Databricks
Location: King of Prussia PA, (Only Locals No relocation) full onsite
We 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 platform.
**Responsibilities**
* 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 updates.
**Required Skills And Qualifications**
* Bachelor’s or Master’s degree in Computer Science, Data Science, or related field.
* Certified 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 Platform.
* Familiarity with vector databases, embeddings, and retrieval-augmented generation (RAG).
* Strong problem-solving, analytical thinking, and communication skills.