Forward Deployed Engineer (FDE) Sr Analyst

Accenture•Cincinnati, OH

1d•Hybrid

About The Position

We are a forward-thinking services company at the forefront of AI-native innovation. We partner with enterprise clients to create next-generation, agent-powered workflows engineered to scale in real-world settings. Our engineers embed deeply with customers, moving projects beyond experimentation into operational reality. You are an AI Native Engineer with a strong foundation in building cloud-native solutions and hands-on experience designing and deploying agentic systems, especially for enterprise environments. You’re a critical thinker who thrives in ambiguity, delivering concrete results by designing, building, and running AI agents that augment workflows and scale across modern infrastructure. You'll shape how enterprises adopt AI-native engineering - either by leading complex agentic solutions and developing engineering talent, or by owning critical technical areas end-to-end as a senior IC.

Requirements

Minimum of 2 years of engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).
Minimum of 1 year of hands-on experience designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production or near-production environments.
Minimum of 1 year of experience with modern AI platforms — OpenAI, Claude, Vertex AI, or open-source models — including building or using abstraction layers for multi-provider pipelines.
Minimum of 3 years strong Python, Java or equivalent experience building 12 factor applications + Infrastructure as Code (Terraform, Helm)
Minimum of 3 years of experience in client-facing communication and collaboration, including leading technical discussions, workshops, or delivery sessions under ambiguity.
Bachelor's degree in Computer Science, Engineering or equivalent OR equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience)

Nice To Haves

Relevant AI certifications or agentic tooling experience are a plus.
You’ve served as an Agentic / AI Engineer in an enterprise environment.
You’ve built multi agent orchestrations using (Lang-graph, Crew AI, Claude SDK, Open AI SDK, etc).
Have a GitHub repo with an agent/plugins you have created
You have additional AI certifications or experience with agentic tooling and frameworks.
You’ve defined or worked with enterprise-grade architectures for compound AI systems, orchestration frameworks, or agent registry / stream-based architectures.
You understand the AI-native paradigm — blending cloud-native with generative model architectures — optimizing for performance, modularity, and efficiency.
You’ve delivered solutions across multiple industries (e.g., finance, healthcare) by tailoring agentic workflows to industry needs.
Driven execution across multiple workstreams, ensuring quality, delivery, and alignment with client outcomes.

Responsibilities

Partner directly with client stakeholders — acting as both technologist and trusted advisor.
Partner with stakeholders to define use cases, rapidly prototype, and deploy agentic workflows that are robust, secure, and operational in complex enterprise domains.
Design and build enterprise-ready AI agents incorporating retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.
Implement resilient, testable, and maintainable agentic workflows that can be iterated on quickly.
Develop and/or extend abstraction layers across AI providers (Anthropic, Google, OpenAI, etc.) to enable seamless integration and multi-provider enablement.
Contribute to shared libraries, SDKs, and patterns that can be reused across clients.
Leverage containerization (Kubernetes, Docker), microservices, serverless, event-driven architectures, CI/CD, and observability stacks to deliver scalable AI-native systems.
Own deployment, monitoring, and troubleshooting for your services in production.
Tailor and deploy agentic applications across verticals (e.g., finance, healthcare, retail), adapting to domain-specific processes and constraints.
Work closely with client SMEs to translate business workflows into agentic solutions.
Participate in and/or lead design workshops, POCs, and code-with sessions to shape data-driven agent workflows with stakeholders, fostering trust and adoption.
Communicate trade-offs, risks, and recommendations clearly to both technical and non-technical audiences.
Define and use key metrics, test harnesses, and evaluation plans to measure agent accuracy, latency, safety, and cost effectiveness.
Iterate rapidly based on data, feedback, and changing requirements.
Craft reusable patterns, documentation, and best practices that influence internal assets and client roadmaps.
Contribute to internal communities of practice around AI-native and agentic engineering.