JD: Python AI Engineer with Databricks exposure

Job Summary

We are seeking a highly skilled Python AI Engineer to join our innovative team. The ideal candidate will be responsible for developing and deploying scalable, production-grade Generative AI applications using Python, modern GenAI frameworks, vector databases, Azure Cloud and  Databricks. You will integrate AI‑powered capabilities into web applications and enterprise platforms, building agentic workflows, RAG systems, and end-to-end AI services.

Key Responsibilities:

• Design, develop, and deploy advanced LLM-powered and agentic AI solutions using Python and modern GenAI frameworks such as LangChain and LangGraph.

• Build and optimize RAG pipelines, including data ingestion, embeddings, retrieval, and vector database integration.

• Develop and manage AI agents capable of tool usage, multistep reasoning, and workflow orchestration.

• Integrate AI services into applications using Python frameworks such as Django, FastAPI, and Flask; build secure REST endpoints, async workers, and SDK-based clients.

• Collaborate with cross‑functional teams to gather requirements, design conversational or agentic flows, and implement scalable AI solutions.

• Monitor and evaluate AI system performance using structured evaluation frameworks, A/B testing, regression testing, and telemetry-driven analysis.

• Continuously optimize AI performance, ensuring correctness, resilience, groundedness, and reliability.

• Develop reusable components, libraries, and pipelines for rapid AI solution deployment.

• Ensure adherence to data privacy, security, and responsible AI engineering standards.

• Utilize Databricks/open source frameworks/libraries for experimentation, development, and scalable GenAI workflows.

Technical Skills and Qualifications:

• BS or MS in Computer Science, Engineering, or a related field.

• Strong programming experience in Python with a focus on building scalable backend systems and APIs.

• Proven experience developing GenAI applications using:

• LangChain, LangGraph, or equivalent frameworks

• RAG architectures and vector databases

• Agentic AI patterns and tool integrations

• MCP (Model Context Protocol) or similar standardized tool interfaces

• Strong understanding of LLMs, NLP techniques, prompting patterns, grounding strategies, and RESTful API integration.

• Practical experience with evaluation techniques including A/B testing, dataset-driven evaluation, and performance monitoring.

• Hands-on experience building applications using FastAPI, Django, or Flask, and observability tools.

• Solid understanding of software engineering best practices: testing, security, logging, monitoring, scalability, and performance tuning.

• (Good to have) Familiarity with Databricks for data workflows and GenAI model development.

• Intermediate to advanced skills in developing secure, reliable, and production-ready AI‑driven systems.

Preferred Qualifications:

• Databricks Certified: Generative AI Engineer Associate or equivalent.

• Experience with prompt engineering, evaluation frameworks, and end-to-end GenAI lifecycle management.

• Knowledge of DevOps/MLOps practices for deploying and maintaining Python-based AI applications.

• Familiarity with agile methodologies and CI/CD pipelines using GitHub or Azure DevOps.

• Full-stack development experience to integrate AI solutions into web applications.evOps and GitHub.