Accenture is shifting from volume-based, multi-year greenfield delivery toward AI-first, sprint-based reinvention. The gap between what we can build and what we actually ship inside client environments is not a strategy problem - it is a behaviour and capability problem.
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.
What You Will Do
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
What We Are Looking For
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
Technical Environment
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)
What Good Looks Like
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.
About Accenture
Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.Visit us at www.accenture.com
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As part of our talent strategy, we hire and develop people who have different backgrounds, different perspectives and different lived experiences. These differences ensure that we have and attract the cognitive diversity to deliver a variety of perspectives, observations and insights which are essential to drive the innovation needed to reinvent, and we hold our leaders accountable for ensuring we have the most innovative and talented people in our industry.
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Based on 7,818 disclosed Software salaries on RoleSuite, the role pays a median of $157K/year, with most offers between $123K and $198K (10th–90th percentile: $102K–$235K).
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