Python AI Engineer with Databricks @ Infobeans

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.