AI-Driven Forward-Deployed Engineer for Design & Digital

Accenture

New York (NY)

On-site

USD 66,000 - 206,000

Full time

13 days ago

Application generator

An application made for this job — a tailored resume and cover letter that speak straight to the posting.

Get past ATS filters

Job summary

Accenture is seeking an AI & Data product leader to work with design and product teams, originating AI-powered digital products that sit on enterprise data foundations and drive revenue and better decisions. You will shepherd use cases through design, prototype, and production, balancing cost, latency, and compliance while engaging senior stakeholders.

The role emphasizes a product-led mindset, full-stack development, and hands-on delivery across GenAI platforms in enterprise settings, with a

Qualifications

Minimum 3 years of AI & Data experience, including solutioning, designing, or delivering AI & Data projects, with hands-on work shipping GenAI features (RAG, agentic workflows, evals and guardrails).
Minimum 3 years of high-value consulting experience, including large-scale delivery with minimal oversight and proven ability to lead workstreams and meet deadlines.
Hands-on delivery experience on at least one enterprise AI platform in production: AWS Bedrock, Google Vertex AI, Azure AI Foundry, or Databricks Mosaic AI.
Working fluency with GenAI frameworks and patterns: LangChain or LlamaIndex, agentic orchestration, MCP, prompt and context engineering, and model selection across frontier and open-weight models (Claude, Gemini, GPT, Llama, Mistral).

Responsibilities

Identify where AI creates measurable business value before a brief exists and originate use cases.
Differentiate through a product-led approach, pairing AI & data depth with product strategy and thinking.
Move from insight to production, aligning design, prototype, and shipping processes.
Design and build AI systems end-to-end: RAG architecture, agentic orchestration, prompt engineering, evals and guardrails.
Select models and approaches, balancing cost, latency, accuracy, and compliance.
Activate AI within client data platforms using data-gravity patterns.
Build AI-powered digital products and experiences full-stack with production accountability.
Deploy responsibly to production: MLOps, model serving, security, cost/latency discipline.
Codify best practices and feed learnings back into offerings.