AI Native Developer (Agentic / Applied)
Role Description
You build the systems that actually make AI work in enterprise environments, not demos, not prototypes that stall after a pilot, but production agentic architectures running inside real client organizations. The difference between an AI Developer and what we are looking for is straightforward: you have shipped a multi-agent system in production, you have owned the eval harness, and you know what happens when your agent fails at 2am because you have lived it.
As an AI Developer (Agentic/Applied), you will design, build, and deploy production-grade agentic AI systems across the full enterprise technology stack. You will work directly with client development teams, lead technical design sessions, and build reusable patterns and accelerators that scale beyond individual engagements.
This role sits at the heart of the AI development talent market — demand is growing faster than supply and will continue to do so. We offer what no single product company can: breadth across every industry, every enterprise technology stack, and every level of organizational complexity, combined with vendor fellowship access inside Anthropic, OpenAI, Microsoft, and Google technical teams and a direct pathway to the Forward Deployed Developer program.
Key Responsibilities
- Design and build production-grade agentic systems end-to-end: multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability
- Build and own RAG pipelines: embeddings, chunking strategy, vector search, context window design and tuning against real quality targets
- Integrate and abstract across multiple LLM providers — OpenAI, Anthropic, Vertex AI, and open-source models — with fallback routing, token, cost, and latency management
- Implement LLM Ops in production: eval harnesses with real quality metrics, prompt versioning, observability tooling (LangSmith, Braintrust, or equivalent), cost and safety monitoring
- Embed directly with client development teams to design, prototype, and deploy agentic solutions — workshops, proofs of concept, code-with sessions, and architecture walkthroughs
- Build reusable patterns, accelerators, and playbooks that scale beyond the individual client engagement and enable the next one to start faster
- Define and use metrics to measure agent accuracy, latency, safety, and cost-effectiveness; present findings and recommendations to client stakeholders in business terms
Basic Qualifications
Required qualifications
- 7+ years of software development experience in production environments
- Minimum of 1 year of experience designing and deploying agentic AI solutions in production environments
- Experience with agentic orchestration frameworks, including LangGraph, CrewAI, AutoGen, or equivalent
- Experience integrating and calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production systems, including provider abstraction, token management, and cost/latency optimization
- Experience building and operating RAG pipelines, including embeddings, chunking strategy, vector search, and context design
- Experience implementing LLM Ops practices, including eval harness design, prompt versioning, and observability tooling
- Experience with cloud-native systems, including Kubernetes, Docker, microservices, serverless architectures, CI/CD, and infrastructure as code (Terraform or Helm)
- Proficiency in Python; experience with Java or equivalent backend language acceptable
- Experience debugging and monitoring production systems
- Experience deploying multiple production agentic AI systems
- Experience defining or standardizing RAG, LLM integration, or LLM Ops practices across projects
- Experience working directly with client development teams in collaborative development environments
- Experience building reusable frameworks, accelerators, or delivery playbooks
- Experience defining and tracking AI system performance metrics such as accuracy, latency, safety, and cost
- Bachelor’s degree or college diploma in a related field.
- English is required for this position as this role will be aligned to multi-national teams where English is the common language across our North America Market. Due to the significant high volume of interactions with these English-speaking multi-national teams, which is inherent to this position, it is not possible to reorganize the company's activities to avoid this requirement.
Compensation at Accenture varies depending on a wide array of factors, which may include but are not limited to the specific office location,
role, skill set, and level of experience. As required by local law, Accenture provides a reasonable range of compensation, based on full-time
employment, for roles that may be hired as set forth below.
The recruiting efforts for this position are intended to fill a brand new position.
The base pay range shown below is intended as a guideline to reflect the majority of offers for this role.
It does not represent a maximum limit — in some cases, actual compensation may exceed the range where appropriate.
Information on benefits is here.
Role Location Annual Salary Range
British Columbia/Ontario/New Brunswick $100,300 to $216,700
#LI-NA-FY25 and #LI-MP
Toronto, Ontario
Calgary, Alberta
Montreal, Quebec
Ottawa, Ontario
Vancouver, British Columbia
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