Senior Databricks AI/ML & Generative AI Engineer - Irving,Texas

Job Description

Job Description

Job Overview

• Work Authorization: Strictly US Citizens (USC) or Green Card (GC) holders only. No H1B, OPT, CPT, or H4-EAD visas accepted.
• Location: Irving, Texas (Hybrid: 3 days a week in-office).
• Pay Rate: $60.00 / hour (depending on experience).
• Experience: 8 to 10 years of professional data engineering/AI experience.

About the Role

We are seeking an experienced Databricks Expert located in the Irving, Texas area to lead the design and deployment of advanced conversational analytics and agentic AI systems. You will utilize Databricks AI/BI tools, focusing heavily on Genie spaces and Vector Search pipelines to deliver real-time enterprise AI solutions across healthcare datasets.

Primary Responsibilities

• Generative AI & LLM Engineering: Design, develop, and deploy AI/ML and Generative AI models for predictive, prescriptive, and generative analytics. Implement advanced architectures including LLMs (GPT, Gemini, LLaMA), Retrieval-Augmented Generation (RAG), and Multi-Agentic workflows using LangChain and LangGraph.
• Databricks Genie & Vector Search: Architect, train, and curate domain-specific Genie spaces with advanced custom skills and semantic instructions. Provision and manage serverless AI/Vector Search endpoints, sync indexes, and optimize chunking/embedding workflows.
• Data Pipeline Engineering: Build and optimize end-to-end pipelines using Python (Sci-kit Learn, Pandas, Flask), PySpark, T-SQL, and SQL across Azure Databricks, Data Factory, Data Lake, and Delta Tables. Integrate data warehousing solutions like Snowflake.
• LLM Security & Responsible AI: Implement guardrails to prevent prompt injections, reduce hallucinations, and ensure safe model outputs. Apply best practices for LLM security, including output moderation, access control, auditability, fairness, transparency, and explainability.
• MLOps Best Practices: Apply model versioning, drift analysis, quantization, and containerization using MLFlow, Docker, and CI/CD pipelines. Maintain code repositories using GitHub/Git in an Agile environment.
• Multi-Cloud & Governance: Deploy and manage GenAI solutions across Azure, AWS (Bedrock, SageMaker), and GCP (Vertex AI). Ensure strict compliance with security and governance standards for healthcare data.
• Leadership: Coach and mentor junior team members.

Required Qualifications

• Education: Bachelor's degree in Computer Science, Information Technology, or a related field.
• AI/ML Expertise: 5+ years of hands-on experience in AI/ML techniques like Prompt Engineering, RAG (Retrieval Augmented Generation), and Agentic AI.
• Core Technical Skills: 5+ years of experience and strong expertise in Python, PySpark, T-SQL, SQL, and big data technologies (Hadoop, Spark).
• Mathematics & Analytics: 2+ years of experience in statistics, data modeling, and simulation.
• GenAI Ecosystem: 1+ years of experience with Generative AI frameworks/architectures (LangChain, HuggingFace, OpenAI APIs).
• Platform Mastery: Minimum 3 years of hands-on experience building production solutions on the Databricks platform, explicitly leveraging Genie and Vector Search / AI Search .
• Cloud Infrastructure: 1+ years of experience with at least one cloud technology: Azure (Databricks, ML Studio), AWS Bedrock, Azure Foundry, Kafka, GCP, or cloud-native AI services.
• DevOps & Deployment: 1+ years of experience with CI/CD pipelines, GitHub Actions, and containerization tools.
• Safety & Alignment: 1+ years of experience with LLM security, prompt engineering, and responsible AI practices.
• Work Authorization: Valid US Citizenship or Permanent Residency (Green Card) is strictly required.

Preferred Qualifications

• Advanced Models: Experience with LLMs (GPT, Gemini, LLaMA) and prompt-based learning.
• Advanced Deep Learning: Knowledge of Kafka, TensorFlow, and advanced deep learning architectures (CNNs, Autoencoders).
• Methodology: Strong understanding of Agile methodologies and DevOps practices.
• Data Strategy: Internal Data management and big data handling experience.
• Soft Skills: Excellent problem-solving skills and the ability to handle ambiguity