Forward Deployed Engineer

Accenture•St. Louis, MO

1d•$26 - $99

About The Position

As a Forward Deployed Engineer at Accenture, you will design, build, and deploy next-generation AI systems powered by foundation models and Large Language Models (LLMs). You will contribute throughout the AI lifecycle, from researching and fine-tuning models to prompt engineering, system integration, and production deployment. The role focuses on improving AI systems’ performance, accuracy, and alignment, and developing and integrating AI systems into products and services. You will work with business leaders, engineers, architects, and designers to align AI systems with business objectives. You will help build responsible, resilient, and scalable AI systems, document technical approaches, and support systems through testing and production. Travel may be required, ranging from 0% to 100% depending on business needs and client requirements.

Requirements

At least 3 years of experience designing and building resilient AI systems or products; implementing controls and guardrails, context engineering, and evaluations in AI systems; using continuous integration and continuous delivery; and applying production best practices such as tracing, logging, and unit testing.
At least 3 years of experience designing and creating AI solutions using patterns such as retrieval-augmented generation (RAG), and handling data pipelines.
At least 3 years of experience on engineering teams using one or more programming languages or frameworks, such as Python, JavaScript, Java, Spring, or Go; and mastery of one or more AI frameworks such as AutoGen, LangGraph, or Semantic Kernel.
At least 3 years of experience with application services from at least one public cloud, such as AWS, GCP, or Azure, including AI and GenAI services and capabilities, or similar platforms such as Anthropic.
At least 2 years of experience in AI engineering building applications or products, with an understanding of fine-tuning, prompt engineering, and performance optimization including token utilization and latency.
At least 1 year of experience leading a team or participating in team management, preferably in AI-driven project environments.
Bachelor’s degree or equivalent (minimum 12 years of work experience). For an associate’s degree, at least 6 years of work experience is required.

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 using Model Context Protocol and Agent to Agent protocols, and understanding evolving agent standards and protocols.
Experience developing applications for mobile and web platforms.
Experience developing Agentic AI systems.

Responsibilities

Design, develop, and optimize AI prompts and applications powered by foundation models, including Large Language Models (LLMs).
Architect and implement generative agent systems using frameworks for multi-model coordination to address complex tasks.
Develop application and component strategies spanning user experience and backend systems.
Define, evaluate, and optimize AI system architectures using relevant frameworks and best practices.
Conduct code reviews, advise on enhancements and issue resolution, and ensure adherence to engineering standards.
Build and maintain scalable machine learning infrastructure, including distributed training pipelines and integrations with APIs and tools.
Evaluate AI systems for robustness, safety, and fairness, and minimize hallucination risks using advanced evaluation methodologies.
Collaborate with business leaders, engineers, architects, and designers to align AI systems with business objectives.
Troubleshoot and resolve issues during testing and in production environments.
Document technical architecture, methodologies, and innovations to support knowledge transfer and ongoing advancement.