SeniorAIEngineer

Senior AI Engineer

Location: Austin, TX 78752, USA
Mode of Work: Hybrid

Last Date for Submission: 7/27

Client : State of TX

Key Responsibilities

• Design, develop, and deploy enterprise AI/ML solutions.
• Architect and implement enterprise search platforms using modern search and retrieval technologies.
• Design, build, and optimize AI agents and intelligent automation workflows.
• Develop AI governance frameworks, technical controls, documentation, and compliance processes.
• Implement Retrieval-Augmented Generation (RAG) solutions and integrate Large Language Models (LLMs) into enterprise applications.
• Build scalable APIs and AI services for production environments.
• Optimize AI model performance, scalability, and reliability.
• Collaborate with engineering, product, and business teams to translate requirements into AI-driven solutions.
• Maintain technical documentation, architecture diagrams, and deployment procedures.
• Stay current with emerging AI technologies, frameworks, and industry best practices.

Required Skills & Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• 5+ years of experience in AI/ML application development.
• Strong experience designing and deploying enterprise search solutions.
• Hands-on experience building AI agents and intelligent automation solutions.
• Experience implementing AI governance, documentation, compliance, and security controls.
• Strong programming experience in Python.
• Experience with Large Language Models (LLMs), Generative AI, RAG, Prompt Engineering, and Vector Databases.
• Experience with AI frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar.
• Experience working with cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform.
• Strong understanding of REST APIs, microservices, and distributed systems.
• Experience with Git, CI/CD pipelines, and Agile development methodologies.
• Excellent analytical, problem-solving, and communication skills.

Preferred Qualifications

• Experience with enterprise AI platforms and AI observability tools.
• Knowledge of AI security, responsible AI, and governance best practices.
• Experience with Docker, Kubernetes, and containerized deployments.
• Familiarity with MLOps tools and model lifecycle management.
• Experience working in enterprise or government environments is a plus.