About the role
AI Lead Engineer - Client Technology role based on the published job description. Key responsibilities and requirements were extracted directly from the posting for quick review.
ConsultingOnsite
Key Responsibilities
- Your key responsibilities • Lead the development and optimization of sophisticated machine learning models • Architect and implement scalable AI systems integrating various data sources • Oversee the setup and management of CI/CD pipelines, Docker environments, and NoSQL databases • Mentor and guide entry to mid-level engineers in best practices and new technologies • Collaborate with cross-functional teams to integrate AI solutions into business processes • Design experiments using advanced scientific methods and validate results rigorously • Lead the design, development, and deployment of AI agents powered by large language models (LLMs) • Oversee prompt engineering strategies and ensure optimal LLM agent performance • Integrate LLM-based agents into enterprise applications and workflows • Establish best practices for evaluating, monitoring, and improving LLM agent outputs Skills and attributes for success • Advanced degree (Master's or Ph.D.) in Computer Science, Engineering, Data Science, Mathematics, or related field (preferred) • Strong leadership skills with a track record of mentoring junior engineers • In-depth knowledge of cloud-based solutions for AI deployment and scaling • Experience with advanced testing strategies including A/B testing, unit tests, and integration tests • Published research or contributions to open-source machine learning projects • Demonstrated experience building and deploying LLM-powered AI agents in production environments • Deep understanding of prompt engineering, LLM fine-tuning, and responsible AI practices Ideally, you'll also • Demonstrated ability to set up and manage Docker images, NoSQL databases, CI/CD systems • Proven expertise in Retrieval Augmented Generation techniques and AI agent platforms • Strong proficiency in Python and deep understanding of ML frameworks like TensorFlow, PyTorch, etc.
- If you can think critically and creatively to solve problems, you will excel.
Requirements
- Your key responsibilities • Lead the development and optimization of sophisticated machine learning models • Architect and implement scalable AI systems integrating various data sources • Oversee the setup and management of CI/CD pipelines, Docker environments, and NoSQL databases • Mentor and guide entry to mid-level engineers in best practices and new technologies • Collaborate with cross-functional teams to integrate AI solutions into business processes • Design experiments using advanced scientific methods and validate results rigorously • Lead the design, development, and deployment of AI agents powered by large language models (LLMs) • Oversee prompt engineering strategies and ensure optimal LLM agent performance • Integrate LLM-based agents into enterprise applications and workflows • Establish best practices for evaluating, monitoring, and improving LLM agent outputs Skills and attributes for success • Advanced degree (Master's or Ph.D.) in Computer Science, Engineering, Data Science, Mathematics, or related field (preferred) • Strong leadership skills with a track record of mentoring junior engineers • In-depth knowledge of cloud-based solutions for AI deployment and scaling • Experience with advanced testing strategies including A/B testing, unit tests, and integration tests • Published research or contributions to open-source machine learning projects • Demonstrated experience building and deploying LLM-powered AI agents in production environments • Deep understanding of prompt engineering, LLM fine-tuning, and responsible AI practices Ideally, you'll also • Demonstrated ability to set up and manage Docker images, NoSQL databases, CI/CD systems • Proven expertise in Retrieval Augmented Generation techniques and AI agent platforms • Strong proficiency in Python and deep understanding of ML frameworks like TensorFlow, PyTorch, etc.
- Extensive experience designing and implementing complex machine learning models and combining ML models with agentic AI frameworks • Expertise with advanced AI pair programming tools (e.g., GitHub Copilot, Cursor) in agent development • Experience architecting solutions that combine LLMs with agentic frameworks and orchestration tools • Familiarity with collaborative coding environments and advanced API integration for LLM agents • Senior analyst-level familiarity with statistics, classical statistical models, and optimization techniques • Knowledgeable in risk management concepts as they relate to AI and machine learning applications What we look for We want people who are self-starters, who can take initiative and get things done.
- Some travel may also be required.