AI Engineer - FDE Software Engineering Sr. Manager

Accenture; New York (NY); On-site; USD 123,000 - 302,000; Full time

Benefits offered by this job

Medical coverage

Dental coverage

Vision coverage

Life insurance

Long-term disability coverage

401(k) plan

Bonus opportunities

Paid holidays

Paid time off

Job summary

Accenture seeks an AI Native Engineer to embed with clients and design, build, and deploy agentic workflows in enterprise environments.

The role spans end-to-end agent architecture, platform integration, and cloud-native engineering with hands-on client engagement through POCs and code-with sessions.

Qualifications

• 10+ years of end-to-end software engineering experience and SDLC expertise.
• 8+ years with programming in Python, Java, or equivalent.
• 8+ years deploying to production with CI/CD and infrastructure as code.
• 8+ years in client communication and collaboration, incl. leading technical workshops.
• 3+ years engineering cloud-native systems (APIs, microservices).
• 3+ years working with AI platforms (OpenAI, Claude) and multi-provider pipelines.
• 2+ years hands-on experience designing Agentic AI solutions.
• 1.5+ years with semantic models, ontologies, or knowledge graphs.
• 1.5+ years with coding agents and AI-assisted software development.
• 1+ year designing and deploying agentic solutions (agents, orchestration).
• Bachelor's degree or equivalent (min 12 years work exp).
• If Associate Degree, min 6 years work exp.

Responsibilities

• Design and engineer enterprise-ready AI agents, including orchestration and routing.
• Build abstraction layers across providers (Anthropic, Google, OpenAI).
• Use containerization and cloud-native patterns to deliver scalable AI-native systems.
• Tailor and deploy agentic applications across finance, healthcare, and retail.
• Run design workshops, POCs, and code-with sessions with stakeholders.
• Define metrics, test harnesses, and evaluation plans for accuracy and latency.
• Create reusable patterns, documentation, and best practices.

Skills

Python

Java

AI agents

Agentic AI

CI/CD

Logging

Monitoring

Client communication

Education

Bachelor's degree or equivalent

Tools

Kubernetes

Docker

Terraform

Helm

APIs

Job description

Accenture is seeking an AI Native Engineer to embed with clients and help design, build, and deploy agentic workflows and AI-native systems in enterprise environments. The work spans end-to-end agent architecture, platform integration, and cloud-native engineering, with hands-on client engagement through POCs and code-with sessions.

What you'll do

• Design and engineer enterprise-ready AI agents including retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.
• Build abstraction layers across AI providers such as Anthropic, Google, OpenAI, and others to support seamless integration and enablement.
• Use containerization and cloud-native patterns including Kubernetes, Docker, microservices, serverless, and event-driven architectures, along with CI/CD and observability to deliver scalable AI-native systems.
• Tailor and deploy agentic applications across verticals such as finance, healthcare, and retail, addressing domain-specific processes through intelligent automation.
• Run design workshops, POCs, and code-with sessions to shape data-driven agent workflows with stakeholders and support trust and adoption.
• Define and apply key metrics, test harnesses, and evaluation plans to assess accuracy, latency, safety, and cost effectiveness.
• Create reusable patterns, documentation, and best practices that can influence internal assets and client roadmaps.

Requirements

• 10+ years of end-to-end software engineering experience and SDLC expertise.
• 8+ years with programming in Python, Java, or equivalent, plus familiarity with evaluation tooling, logging, monitoring, and agent observability.
• 8+ years deploying to production, including CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging.
• 8+ years in client communication and collaboration, including leading technical workshops and delivering under ambiguity.
• 3+ years engineering cloud-native systems, including APIs, microservices, containerization, and serverless.
• 3+ years working with AI platforms such as OpenAI and Claude, and open-source models, including building abstraction layers to manage multi-provider pipelines.
• 2+ years hands-on experience designing and delivering Agentic AI solutions.
• 1.5+ years experience with semantic models, ontologies, knowledge graphs, or enterprise knowledge.
• 1.5+ years experience using coding agents and AI-assisted software development.
• 1+ year expertise designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.
• Bachelor's degree or equivalent (minimum 12 years work experience). If Associate Degree, must have minimum 6 years work experience.

Technologies

• Python, Java, Anthropic, Google, OpenAI, Claude
• Kubernetes, Docker
• Microservices, serverless, event-driven architectures
• CI/CD, observability, Terraform, Helm
• APIs, open-source models
• Semantic models, ontologies, knowledge graphs
• RAG, AI agents

Location and role details

• Location: New York, NY (onsite)
• Compensation: USD 122,700 - 302,400 per year
• Travel: Travel may be required for this role, varying from 0 to 100% based on business need and client requirements.

Benefits

• Medical, dental, vision, life, and long-term disability coverage
• 401(k) plan
• Bonus opportunities
• Paid holidays
• Paid time off

Bonus points

• Experience as an Agentic AI Engineer in an enterprise environment
• Additional AI certifications or agentic tool experience
• Experience defining or working with enterprise-grade architectures for compound AI systems, orchestration frameworks, or agent registry/stream-based architectures
• Understanding of the AI-native paradigm, blending cloud-native with generative model architectures optimized for performance, modularity, and efficiency
• Delivered solutions across multiple industries (for example, finance and healthcare) by tailoring agentic workflows to industry needs