AI Native Software Engineering Senior Analyst

Accenture•Columbus, OH

4d•Hybrid

About The Position

Accenture’s Advanced Technology Centers (ATCs) help clients address business challenges through technology, industry expertise, and innovation. In this role, you will build cloud-native solutions and design, deploy, and operate agentic systems for enterprise environments. You will work with client stakeholders as both a technologist and trusted advisor, shaping use cases, prototyping solutions, and deploying robust and secure agentic workflows within complex client environments. The role includes designing enterprise-ready AI agents and reusable agentic workflows; integrating multiple AI providers and contributing to shared libraries and SDKs; and delivering cloud-native systems using containers, microservices, serverless and event-driven architectures, CI/CD, and observability. You will tailor workflows to client domains, collaborate with subject-matter experts, participate in design workshops and proofs of concept, communicate technical trade-offs, and measure and improve agent accuracy, latency, safety, and cost effectiveness. You will also document reusable practices and contribute to internal communities of practice. This is a hybrid role requiring three days per week in the office. The primary location is Columbus, Ohio; qualified applicants may also be considered in Tampa, Atlanta, Chesterbrook, Dallas, or Houston. Travel may be required and will vary from 25% to 75% according to business and client needs.

Requirements

At least 2 years of engineering experience with cloud-native systems, including APIs, microservices, containerization, or serverless.
At least 2 years of experience with Python, Java, or equivalent, building 12-factor applications, and Infrastructure as Code such as Terraform or Helm.
At least 6 months of hands-on experience designing and deploying agentic solutions, such as agents, orchestration, context engineering, RAG, or workflows, in production or near-production environments.
At least 6 months of experience with modern AI platforms such as OpenAI, Claude, Vertex AI, or open-source models, including building or using abstraction layers for multi-provider pipelines.
At least 1 year of client-facing communication and collaboration experience, including leading technical discussions, workshops, or delivery sessions in ambiguous situations.
Bachelor’s degree in Computer Science, Engineering, or equivalent; alternatively, at least 12 years of work experience. Applicants with an associate degree must have at least 6 years of work experience.

Nice To Haves

Relevant AI certifications or experience with agentic tooling.
Experience serving as an Agentic or AI Engineer in an enterprise environment.
Experience building multi-agent orchestrations using tools such as LangGraph, CrewAI, Claude SDK, or OpenAI SDK.
A GitHub repository containing an agent or plugins you have created.
Additional AI certifications or experience with agentic tooling and frameworks.
Experience defining or working with enterprise-grade architectures for compound AI systems, orchestration frameworks, agent registries, or stream-based architectures.
Understanding of the AI-native paradigm, combining cloud-native and generative model architectures while optimizing performance, modularity, and efficiency.
Experience delivering solutions across multiple industries by tailoring agentic workflows to industry needs.
Experience driving execution across multiple workstreams while ensuring quality, delivery, and alignment with client outcomes.

Responsibilities

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 or extend abstraction layers across AI providers to enable seamless integration and multi-provider use.
Contribute reusable shared libraries, SDKs, and engineering patterns.
Deliver scalable AI-native systems using containerization, microservices, serverless and event-driven architectures, CI/CD, and observability stacks.
Own deployment, monitoring, and troubleshooting of services in production.
Tailor agentic applications to industry-specific processes and constraints, and work with client subject-matter experts to translate workflows into agentic solutions.
Participate in or lead design workshops, proofs of concept, and code-with sessions with stakeholders.
Explain technical trade-offs, risks, and recommendations to technical and non-technical audiences.
Define and use metrics, test harnesses, and evaluation plans to assess agent accuracy, latency, safety, and cost effectiveness.
Iterate based on data, feedback, and changing requirements.
Create reusable patterns, documentation, and best practices, and contribute to internal AI-native and agentic engineering communities.