Titanium Engineer Manager

Accenture•Seattle, WA

39d

About The Position

As a Titanium Engineer Manager, you will play a pivotal role in designing, building, and deploying next-generation AI systems powered by Large Language Models (LLMs). You will contribute across the full AI lifecycle - from researching and fine-tuning foundation models to prompt engineering, system integration, and deployment into production environments. You will improve performance, accuracy and alignment of the LLMs and AI systems. You bring a mix of hands-on engineering skills, deep knowledge of modern AI architectures, and a passion for applying AI responsibly to solve real-world problems. In addition, you will utilize your strong skills to develop and integrate AI Systems into products and services. Have expertise in design, develop and optimizing AI prompts.

Requirements

Minimum of 4 years of demonstrated expertise in successfully designing and building resilient AI systems or products, implementing controls and guardrails, context engineering, utilizing Evals in AI systems, Continuous Integration, Continuous Delivery, and experience implementing best practices in implementing AI systems in production such as tracing, logging and unit testing.
Minimum 4 years of experience designing and creating AI Solutions using design patterns like retrieval augmentation generation (RAG) and handling data pipelines.
Minimum of 4 years of experience in Engineering teams with one or more programming languages and frameworks, such as Python, JavaScript, Java, Spring or GoLang, showcasing a strong command over the technical foundations and mastery of one or more AI Frameworks like Autogen, LangGraph or Semantic Kernel and others.
Minimum of 4 years of experience working with application services from at least one public cloud (AWS, GCP, Azure, etc.), including use of AI and GenAI services and capabilities on these or similar platforms such as Anthropic.
Minimum of 3 years of experience working in AI Engineering building applications or products with strong understanding of fine tuning & prompt engineering, performance optimization including token utilization and latency.
Minimum of 2 years of experience leading a team or being part of team management, preferably in AI-driven project environments.
Bachelor's degree or equivalent (minimum 12 years) work experience. (If associate's degree, must have minimum 6 years' work experience)

Nice To Haves

Experience training, fine-tuning, and evaluating LLMs and multimodal foundation models using advanced techniques such as self-supervised and transfer learning.
Experience designing value justification frameworks for AI solutions, including cost estimation, ROI calculation, and tradeoff analysis.
Experience building AI solutions that utilize Model Context Protocol, Agent to Agent protocols and understanding of evolving agent standards and protocols.
Experience developing applications for both mobile and web platforms, showcasing versatility and adaptability in diverse environments.
Experience developing Agentic AI systems.

Responsibilities

Design, develop, and optimize AI prompts and next-generation applications powered by foundation models, including Large Language Models (LLMs).
Architect and implement generative agent systems using frameworks for multi-model coordination to tackle complex tasks.
Develop application and component strategies, overseeing both user experience and backend systems.
Define, evaluate, and optimize AI system architectures, leveraging relevant frameworks and best practices.
Conduct thorough code reviews, provide expert guidance on enhancements and issue resolution, and ensure adherence to engineering standards.
Build and maintain scalable machine learning infrastructure, including distributed training pipelines and seamless integration with APIs and tools.
Apply advanced evaluation methodologies to ensure model robustness, safety, fairness, and minimize hallucination risks.
Collaborate closely with cross-functional teams-including business leaders, engineers, architects, and designers-to align AI systems with business objectives.
Support troubleshooting and issue resolution during testing phases as well as in production environments.
Document technical architecture, methodologies, and innovations for effective knowledge transfer and ongoing advancement.