Gen AI - Engineering Lead

Design and implement server-side logic, define and maintain databases, and integrate user-facing elements developed by front-end developers. Ensure high performance and responsiveness of applications, and implement security and data protection measures.

• 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 .
• Bachelor’s degree in Engineering, Computer Science, AI/ML, Data Science, or a related technical field .
• 10+ years of overall professional experience in software engineering, machine learning, or AI engineering.
• Minimum 3+ years of hands-on experience working with Generative AI technologies and LLMs .
• Strong programming experience in Python .
• Proven experience fine-tuning, deploying, and optimizing large language models .
• Experience deploying Generative AI solutions into production environments .
• Hands-on experience working with Linux-based systems .
• Experience with cloud platforms such as Microsoft Azure or Google Cloud Platform (GCP) .
• Experience building ML pipelines and deploying AI models using modern MLOps practices .
• Experience handling and securing sensitive data such as PII and PHI in compliance with security and regulatory requirements.