AI Engineering Consultant - Utilities

Accenture•Boston, MA

1d•$54,400 - $205,800•Hybrid

About The Position

Accenture's AI and Data practice is seeking AI Engineers who can build and integrate AI applications using leading model providers and cloud platforms. The role involves developing LLM-powered applications, APIs, and pipelines, implementing patterns like RAG, prompt orchestration, evaluation, and guardrails. Successful candidates will deploy and operationalize AI workloads on cloud infrastructure, focusing on scalability, security, cost efficiency, and reliability. This role requires practitioners who understand enterprise operations and can engineer AI solutions to transform business processes and outcomes, rather than generalists advising on AI in the abstract. Accenture is a leading global professional services company with approximately 790,000 employees serving clients in over 120 countries, focused on helping businesses build their digital core, optimize operations, accelerate revenue growth, and enhance citizen services.

Requirements

Minimum of 3 years of experience in software or AI/ML engineering
Minimum of 2 years of hands-on experience building AI applications with providers such as OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, or Google Vertex AI
Minimum of 2 years of experience with Python and API integration
Minimum of 2 years of experience with RAG, vector databases, and orchestration frameworks (e.g., LangChain, LlamaIndex)
Minimum of 1 year of experience with containerization (Docker/Kubernetes), CI/CD, and cloud security
Minimum of 1 year of experience serving utilities clients (electric, gas, or water) or in a utilities finance, controllership, or regulatory function.
Bachelor's degree or equivalent (minimum 12 years' work experience). If Associate’s Degree, must have equivalent minimum 6-year work experience.

Nice To Haves

Master’s degree in a relevant field
Cloud or AI engineering certifications (AWS, Azure, or Google)
Experience with agentic AI patterns and multi-agent orchestration

Responsibilities

Build AI applications: develop and integrate AI applications using leading model providers (OpenAI, Anthropic) and cloud platforms (AWS Bedrock, Azure OpenAI, Google Vertex AI).
Implement LLM patterns: implement RAG, prompt orchestration, evaluation, and guardrails in LLM-powered applications, APIs, and pipelines.
Deploy AI workloads: deploy and operationalize AI workloads on cloud infrastructure with scalability, security, cost efficiency, and reliability in mind.
Engineer with modern tooling: use Python, API integration, vector databases, and orchestration frameworks (e.g., LangChain, LlamaIndex) to build production-ready solutions.
Apply DevOps practices: apply containerization (Docker/Kubernetes), CI/CD, and cloud security best practices across the AI delivery lifecycle.