Forward Deployed Engineers are the forcing function. They embed directly inside accounts, operate at pace, and build working production-grade solutions that reset client expectations of what delivery looks like and how fast it can move.

This is not a support role and it is not an advisory role. FDEs are senior technical practitioners who own outcomes end-to-end, pull AI tools forward into real delivery contexts, and bring hard field intelligence back into Tech RE.

As a Forward Deployed Engineer, you will embed directly into Accenture's priority accounts across ANZ MU. You are the technical owner on the ground: scoping problems, building solutions, closing the AI last mile, and demonstrating what AI-native delivery actually looks like.

You work at the intersection of engineering, product thinking, and client delivery. The expectation is that you can go from a vague client problem to a working proof of concept in days - and from there to a production-grade deployment in weeks.

Embed and Build

• Sit inside client teams as the primary technical owner from discovery through to production deployment

• Translate ambiguous business problems into a concrete technical plan, scoped and sequenced for sprint delivery

• Build working prototypes rapidly — not polished decks — and iterate based on real feedback from real users

• Deploy production-grade AI solutions on client infrastructure, integrating into legacy systems, regulated data environments, and existing identity and security frameworks

• Work across the full stack: frontend, backend, APIs, data pipelines, and agentic workflows

• Develop applications and workflows that integrate structured and unstructured enterprise data

Drive AI-Native Engineering

• Lead with AI tools — coding assistants, agent frameworks, LLM-based workflows — as the default method of delivery, not an add-on

• Design and deploy enterprise AI solutions incorporating LLMs, Agentic AI, RAG, semantic search, workflow orchestration, and intelligent automation

• Define scalable architectures across cloud platforms and enterprise AI ecosystems

• Establish approaches for prompt engineering, model evaluation, governance, security, and responsible AI in production environments

• Champion AI-enabled delivery across planning, development, testing, documentation, and operations

• Drive continuous improvement through rapid experimentation and user feedback loops

Technical Leadership

• Provide technical leadership across delivery programmes — architecture reviews, design decisions, technology selection

• Establish engineering standards, reusable assets, accelerators, and best practices that travel across engagements

• Mentor engineers and raise the delivery capability of client and Accenture teams working alongside you

• Codify patterns and delivery accelerators from each engagement for use across the broader Tech RE practice

• Surface product gaps, delivery friction, and emerging client needs back to Tech RE leadership as structured field intelligence

Non-Negotiable

• You have shipped working software to real users in high-stakes environments, not just demos or internal tools - Proven production coding capability

• Strong hands-on Python and/or TypeScript; comfort across frontend and backend; cloud infrastructure fluency (AWS, Azure, or GCP)

• Hands-on experience building with LLMs or AI agent frameworks in a production or near-production context

• Strong data engineering fundamentals — pipelines, structured and unstructured datasets, reasoning about data quality in messy real-world environments

• Experience designing and deploying enterprise AI applications including agentic workflows, RAG pipelines, vector databases, embeddings, and semantic search

• Ability to navigate ambiguity and drive outcomes at pace — you would rather build and learn than specify and wait

Strongly Preferred

• Experience deploying solutions inside enterprise environments with legacy systems, data governance constraints, and change management overhead

• Experience in regulated industries — financial services, health, government — and the operational constraints that come with them

• Prior consulting, customer success, or forward-deployed role where you owned technical outcomes in a client-facing setting

• Track record as an early engineer or technical founder who has built from zero to production

• Experience with cloud-native and enterprise-scale architecture initiatives across multi-cloud environments

How You Work

• You own outcomes, not tasks — if something is not working, you diagnose it and fix it

• You move fast and are comfortable with ambiguity — you would rather build and learn than specify and wait

• You communicate clearly with both technical and non-technical audiences without oversimplifying or padding

• You are comfortable being in the room with senior client stakeholders as a technical peer, not a support function

• You are resilient under pressure and keep momentum when projects get messy

FDEs are expected to be tool-agnostic and platform-fluent. The following reflects the environment you will commonly operate in across our client base.

AI Frameworks and Orchestration

• LLM APIs: Anthropic Claude, OpenAI, Google Gemini, Azure OpenAI

• Agent frameworks: LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, AWS Agentcore, Google ADK, Microsoft Agent Framework

• RAG and retrieval: vector databases (Pinecone, pgvector, Weaviate, Qdrant), semantic search, embeddings

• Workflow orchestration and intelligent automation tooling

Cloud Platforms

• AWS — Lambda, Step Functions, Bedrock, SageMaker, ECS

• Azure — Azure OpenAI Service, Azure AI Foundry, Azure ML

• GCP — Vertex AI, Agent Builder, Vertex AI Search

• Cross-cloud deployment, IaC (Terraform, Pulumi), CI/CD pipelines

Enterprise AI Platforms

• Palantir Foundry and AIP — experience deploying AI applications on Foundry, building ontologies, operating AIP agents

• Salesforce AI and Einstein — integration of AI capabilities into Salesforce CRM and FSC environments

• ServiceNow AI — AI-enabled workflow automation and Now Intelligence

• SAP AI Core / Joule — AI integration in SAP BTP and S/4HANA environments

• Microsoft Copilot Studio and Power Platform — enterprise AI automation and copilot development

• Databricks — unified analytics and ML platform, Delta Lake, MLflow

• Snowflake — data engineering and Cortex AI capabilities

Engineering Stack

• Languages: Python (primary), TypeScript/JavaScript, Java or .NET for enterprise integration contexts

• Backend: FastAPI, Node.js, microservices, REST and GraphQL APIs

• Frontend: React, TypeScript — sufficient to build and ship end-to-end

• Data: SQL/NoSQL, Snowflake, Databricks, data pipelines, streaming (Kafka, Kinesis)

• DevOps: Docker, Kubernetes, CI/CD, observability tooling (OpenTelemetry, Langfuse)

The benchmark for this role is the model pioneered by Palantir and adopted by OpenAI, Salesforce, and Anthropic: elite engineers embedded in client environments who close the gap between platform capability and real-world impact.

A strong FDE in their first 90 days will have: embedded in at least one account sprint, shipped a working proof of concept that the client can demo to their own leadership, and identified at least one reusable delivery pattern codified for the broader practice.

Over time, FDEs become the practitioners who reshape how clients think about what delivery pace is possible — and how Accenture is different.