Gen AI - Engineering Lead

Responsibilities

• Design, develop, and deploy Generative AI and machine learning solutions using large language models and modern AI frameworks.
• Implement LLM fine-tuning, prompt engineering, retrieval-augmented generation (RAG), and model optimization techniques.
• Deploy and manage AI/ML pipelines and GenAI applications in production environments using cloud platforms such as Azure or GCP.
• Build and maintain agentic AI systems leveraging modern orchestration and agent frameworks.
• Work with large datasets to build data pipelines and ensure effective model training and evaluation.
• Develop AI solutions using Python and modern ML libraries.
• Manage deployments and infrastructure in Linux-based environments.
• Ensure secure processing and governance of sensitive data, including PII and PHI, adhering to enterprise security and compliance standards.
• Optimize AI models and infrastructure for performance, scalability, reliability, and cost efficiency.
• Collaborate with cross-functional teams to integrate AI models into enterprise applications and production systems.
• Document model architectures, experiments, deployment pipelines, and operational processes.
• Stay current with emerging developments in Generative AI, LLMs, and AI engineering practices.
• - Design, develop, and deploy Generative AI and machine learning solutions using large language models and modern AI frameworks. - Implement LLM fine-tuning, prompt engineering, retrieval-augmented generation (RAG), and model optimization techniques. - Deploy and manage AI/ML pipelines and GenAI applications in production environments using cloud platforms such as Azure or GCP. - Build and maintain agentic AI systems leveraging modern orchestration and agent frameworks. - Work with large datasets to build data pipelines and ensure effective model training and evaluation. - Develop AI solutions using Python and modern ML libraries. - Manage deployments and infrastructure in Linux-based environments. - Ensure secure processing and governance of sensitive data, including PII and PHI, adhering to enterprise security and compliance standards. - Optimize AI models and infrastructure for performance, scalability, reliability, and cost efficiency. - Collaborate with cross-functional teams to integrate AI models into enterprise applications and production systems. - Document model architectures, experiments, deployment pipelines, and operational processes. - Stay current with emerging developments in Generative AI, LLMs, and AI engineering practices.