As an Applied AI team member at Accenture, you will be a Pre-Sales architect focused on becoming a trusted technical advisor helping large enterprises understand the value of Claude and paint the vision on how they can successfully integrate and deploy Claude into their technology stack. You'll combine your deep technical expertise with customer-facing skills to architect innovative LLM solutions that address complex business challenges while maintaining our high standards for safety and reliability.
The Work
Working closely with our Sales, Product, and Engineering teams, you'll guide customers from initial technical discovery through successful deployment. You'll leverage your expertise to help customers understand Claude's capabilities, develop evals, and design scalable architectures that maximize the value of our AI systems.
Key Responsibilities
Partner with account executives to deeply understand customer requirements and translate them into technical solutions, ensuring alignment between business objectives and technical implementation
Serve as the primary technical advisor to enterprise customers throughout their Claude adoption journey, from discovery to initial evaluation through deployment. You will need to coordinate internally across multiple teams & stakeholders to drive customer success
Create and deliver compelling technical content tailored to different audiences — from technical deep dives for engineering & development teams to business value focused conversations with executives and UI/UX focused conversations with designers
Help customers develop evaluation frameworks (evals) to measure Claude's performance for their specific use cases – apply advanced evaluation methodologies and code reviews to ensure model robustness, safety, fairness, and minimized hallucination risk
Define, evaluate, and optimize AI system architectures, leveraging relevant frameworks and best practices, and guide customers in integrating AI effectively into their existing technology stack
Build, support, and maintain scalable machine learning infrastructure, including distributed training pipelines and seamless and integration with APIs and tools
Document technical architecture, methodologies, and innovations for effective knowledge transfer and continuous improvement.
Identify common integration patterns and contribute insights back to Accenture's Product and Engineering teams.
Maintain strong knowledge of the latest developments in LLM capabilities and implementation patterns
Travel may be required for this role. The amount of travel will vary from 0 to 100% depending on business need and client requirements.
Here's What You Need
Minimum of 1 year of demonstrated expertise designing and building resilient AI systems or products using Claude/codex — implementing controls and guardrails, context engineering, evals, CI/CD, and production best practices such as tracing, logging, and unit testing.
Minimum of 5 years of experience in technical customer-facing roles such as Solutions Architect, Sales Engineer, or Technical Account Manager
Minimum of 3 years of experience leading a team or contributing to team management, preferably in AI-driven project environments.
Minimum of 3 years of experience in AI engineering, building applications or products with strong understanding of fine-tuning, prompt engineering, and performance optimization (token utilization, latency).
Minimum of 5 years of experience in engineering teams with one or more programming languages and frameworks (Python, Java, C, Go, etc.), and mastery of one or more AI tools such as Claude, Codex, Antigravity, Gemini, etc.
Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate's Degree, must have minimum 6 years work experience)
Nice to have:
Experience working with enterprise customers and navigating complex buying cycles involving multiple stakeholders.
Exceptional ability to build relationships and communicate technical concepts to diverse audiences — including C-suite executives, engineering and IT teams, and business stakeholders.
Passion for thinking creatively about how to use technology in a way that is safe and beneficial, and ultimately furthers the goal of advancing safe AI systems.
Excitement for engaging in cross-organizational collaboration, working through trade-offs, and balancing competing priorities.
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 trade-off analysis.
Experience building AI solutions that leverage Model Context Protocol (MCP), agent-to-agent protocols, and evolving agent standards.
Experience developing applications for both mobile and web platforms.
Familiarity with common LLM frameworks and tools, or a background in machine learning or data science.
Comfort with Python for prototyping and customer-facing technical work.
A love of teaching, mentoring, and helping others succeed, and a passion for using technology in a way that is safe, beneficial, and advances responsible AI.