Generative 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.

• 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.

• 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.