We are looking for a skilled Agentic AI + Databricks Engineer to design, develop, and deploy intelligent AI-powered applications using agentic AI frameworks and the Databricks platform. The ideal candidate will have experience in AI, data engineering, and building autonomous AI agents that can reason, plan, use tools, and execute multi-step workflows.
The candidate will work on building scalable AI solutions by combining Databricks' data and AI capabilities with modern agentic AI architectures.
Key Responsibilities
Design, develop, and deploy AI agents capable of reasoning, planning, and executing multi-step tasks.
Build agentic AI applications using LLMs, Retrieval-Augmented Generation (RAG), and frameworks such as LangChain, LangGraph, or similar technologies.
Develop and maintain data pipelines using Databricks, Apache Spark, PySpark, and Delta Lake.
Leverage Databricks Mosaic AI and Model Serving to build, evaluate, and deploy AI-powered applications.
Implement RAG pipelines using vector search, embeddings, document processing, and semantic retrieval.
Integrate AI agents with APIs, databases, enterprise applications, and external tools.
Work with structured and unstructured data to support AI-driven use cases.
Implement prompt engineering, context management, memory, and tool-calling capabilities.
Apply responsible AI practices, including guardrails, access control, monitoring, and evaluation.
Optimize AI application performance, scalability, reliability, and cost.
Collaborate with data engineers, ML engineers, architects, and business stakeholders to deliver production-ready AI solutions.
Required Skills and Qualifications
Strong programming experience in Python.
Hands-on experience with Databricks, Apache Spark, PySpark, and Delta Lake.
Practical experience building applications using LLMs and Generative AI.
Understanding of agentic AI concepts, including planning, reasoning, tool use, and multi-agent workflows.
Experience with frameworks such as LangChain, LangGraph, LlamaIndex, or equivalent.
Experience implementing RAG pipelines, embeddings, vector databases, and semantic search.
Familiarity with REST APIs, SQL, data processing, and integration patterns.
Understanding of MLflow or similar tools for experiment tracking, model management, and evaluation.
Experience with cloud platforms such as AWS, Azure, or Google Cloud.
Knowledge of software engineering best practices, Git, testing, CI/CD, and production deployment.
Education
Bachelor's or Master's degree in Computer Science, Data Science, Engineering, IT, Statistics, Mathematics, Business Analytics, or related field.
Python, SQL, LLMs and Generative AI, LangChain, LangGraph, RAG pipelines, AWS Glue, Lambda, S3, Redshift, Athena, Kinesis, Step Functions, Databricks, Apache Spark, PySpark, and Delta Lake.