AI Lead; Generative AI, LLM & Agentic AI Irving, TX or Tampa, FL

Job Title:

AI Lead (Generative AI, LLM & Agentic AI) | Tanisha Systems | Irving, TX or Tampa, FL, USA

Recruiting Company:
Tanisha Systems

Job Location:

Irving, Texas or Tampa, Florida, USA

Job Type: Contract (W2 or C2C)

Number of Positions: 15

Contract Duration:
Long-Term

Experience

Required:

10+ Years Total Experience with 2–5 Years in Technical Leadership Roles

Work Mode:
Onsite

Interview Process: 2 Rounds

Industry: Artificial Intelligence, Machine Learning & Enterprise Technology

Tanisha Systems is seeking experienced AI Leads to spearhead the design, development, and deployment of next-generation Generative AI solutions. This role is ideal for technology leaders with deep expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and enterprise-scale AI platforms who can drive innovation while leading high-performing engineering teams.

As an AI Lead, you will be responsible for architecting and delivering advanced AI solutions that leverage state-of-the-art Generative AI technologies. You will work closely with business stakeholders, solution architects, data scientists, and engineering teams to develop scalable AI applications powered by Large Language Models, RAG frameworks, and multi-agent systems. The role requires strong hands-on expertise in Python-based AI development, machine learning frameworks, cloud-native architectures, and containerized deployments.

You will lead the full AI development lifecycle, from solution design and model integration to deployment, optimization, and production support. This is an exceptional opportunity to contribute to large-scale AI transformation initiatives and shape the future of enterprise AI adoption.

• Lead the design, development, and implementation of enterprise Generative AI solutions.
• Architect and deploy Large Language Model (LLM) applications for business and customer-facing use cases.
• Design and implement Retrieval-Augmented Generation (RAG) frameworks to improve response quality and contextual accuracy.
• Develop and manage multi-agent and Agentic AI systems to support complex workflows and intelligent automation.
• Build AI-powered applications using Lang Chain, Llama Index, Hugging Face, and modern AI frameworks.
• Lead AI engineering teams through project planning, execution, and delivery.
• Develop scalable machine learning and deep learning solutions using Python and industry-standard frameworks.
• Integrate AI solutions with enterprise platforms, APIs, databases, and cloud services.
• Deploy AI applications using Docker and containerized environments.
• Optimize model performance, inference processes, scalability, and operational efficiency.
• Establish AI development standards, governance practices, and best practices across projects.
• Collaborate with stakeholders to translate business requirements into innovative AI solutions.
• Conduct technical reviews, mentoring, and knowledge-sharing sessions with engineering teams.
• Support production deployment, monitoring, troubleshooting, and continuous improvement initiatives.

• 10+ years of overall software engineering, machine learning, or AI development experience.
• 2–5 years of experience leading AI, Machine Learning, or Data Science teams.
• Strong expertise in Generative AI technologies and Large Language Models (LLMs).
• Hands-on experience designing and implementing RAG architectures.
• Experience building Multi-Agent and Agentic AI solutions.
• Advanced Python programming skills.
• Strong experience with PyTorch, Tensor Flow, and Keras.
• Experience using Lang Chain for AI application development.
• Experience with Hugging Face models, libraries, and deployment frameworks.
• Experience with Llama Index and knowledge retrieval architectures.
• Hands-on experience with Docker and containerized application deployment.
• Strong understanding of Git-based source control and collaborative development workflows.
• Knowledge of AI system architecture, deployment patterns, and optimization techniques.
• Strong analytical, problem-solving, and technical leadership skills.
• Excellent communication and stakeholder management capabilities.

• Experience deploying LLMs in cloud environments such as AWS, Azure, or Google Cloud.
• Knowledge of Vector Databases and semantic search technologies.
• Experience with MLOps, CI/CD pipelines, and AI model lifecycle management.
• Exposure to NVIDIA GPU acceleration and distributed AI workloads.
• Knowledge of Responsible AI, AI Governance, and AI Security best practices.
• Experience building enterprise chatbots, copilots, and intelligent automation solutions.
• Familiarity with Kubernetes and cloud-native AI deployment architectures.