Forward Deployed Engineer - Products

Accenture•New York, NY

4d•$138,800 - $434,000•Hybrid

About The Position

Accenture is building a new portfolio of agentic software products in the industries we have spent decades helping to transform. We begin with advantages most product companies do not have: deep industry expertise, trusted relationships with many of the world's largest organizations, and direct access to the complex workflows and problems that matter most to them. That allows us to build closely with customers while creating repeatable products that can be taken broadly to market. We are forming small, highly capable product teams to do this. These teams bring product management, design, and engineering together with the autonomy to move quickly, make decisions, and own outcomes from the earliest prototype through production and scale.

Requirements

Minimum of 3 years of experience building and shipping production software, or equivalent demonstrated experience.
Minimum of 5 years of experience and strong software engineering fundamentals, including APIs, data modeling, integrations, cloud-native development, security, and debugging in production environments.
Minimum of 5 years of experience working directly with enterprise customers in a technical capacity — implementation, solutions engineering, technical account management, or equivalent.
Minimum of 5 years of experience and demonstrated ability to build integrations between enterprise systems, including REST and GraphQL APIs, identity providers, data pipelines, and common SaaS platforms.
Minimum of 1 year of substantive, hands-on experience working with AI or agentic systems in a practical context — configuring LLM-based products, building with LLM APIs, implementing retrieval systems, or deploying AI workflows in production.
Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience)
A practical understanding of how enterprise data environments work: access controls, compliance constraints, data residency, security requirements, and the organizational dynamics that govern what can be changed and what cannot.
Strong written and verbal communication skills. Able to explain technical systems clearly to both technical implementors and non-technical stakeholders. Able to write crisp, useful documentation.
The ability to operate independently in complex, ambiguous environments without waiting for complete information or a clear playbook.
A genuine bias toward customer outcomes — not just customer satisfaction. Able to tell the difference between what a customer asks for and what they actually need.
Demonstrated ability to use AI development tools to materially increase engineering leverage.

Responsibilities

Embed with customers and design partners. Spend substantial time on-site at customer and design partner organizations. Develop deep relationships with the technical and operational teams who are using or evaluating the product. Understand their workflows, constraints, data environments, integration landscape, and the specific problems they are trying to solve. Become a trusted technical presence.
Drive implementation and time-to-value. Own the customer’s technical journey from signed agreement through successful production deployment. Build and configure integrations between our products and customer systems, including authentication, data pipelines, APIs, identity providers, and existing enterprise software. Resolve implementation blockers quickly and keep customers moving forward.
Build on top of the product. Write code to extend the product for specific customer contexts — custom agents, workflow automations, tool integrations, evaluation harnesses, and operational dashboards. Know when a customer need should be met with customer-specific tooling versus fed back as a product requirement. Resist one-off customization that cannot scale; push toward repeatable patterns.
Configure and deploy agentic systems. Configure, tune, and deploy AI-native product capabilities within customer environments, including agent behavior, tool access, guardrails, escalation logic, human-in-the-loop workflows, and evaluation criteria. Help customers define what good outputs look like and build the systems to measure and improve against that bar. Operate AI systems responsibly in enterprise contexts with real data and real consequences.
Solve problems on the ground. Find and fix issues before they become customer escalations. Debug integration failures, model behavior problems, data quality issues, performance bottlenecks, and configuration errors. Escalate promptly when a problem requires product changes. Never let a customer sit on a problem you can resolve. Document what you find in ways that are useful to the product team.
Be the voice of the customer in product. Translate what you observe in the field into clear, evidence-based product feedback. Distinguish between problems that are unique to one customer’s environment and patterns that represent a gap in the product. Bring specific workflow observations, failure modes, and unmet needs back to the product team in forms they can act on. Make the product better for every customer by representing the customers you are embedded with.
Enable customer teams. Build the knowledge, documentation, and tooling that allows customer teams to operate the product independently over time. Run training sessions, write runbooks, create evaluation frameworks, and establish monitoring practices. The goal is a customer who is capable and confident — not one who is permanently dependent.
Operate AI-native. Use AI tools as a core part of your daily technical workflow. Apply them to code generation, debugging, documentation, data analysis, and customer communication. Understand their limits. Build repeatable workflows and context that increase your own leverage across multiple customer engagements.