AI Native Solutions Lead (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 Solutions Lead 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 Solutions Lead (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 Solutions Lead program.
Key Responsibilities
- Architect and govern production-grade agentic systems at enterprise scale: multi-agent orchestration across complex environments, RAG pipelines, policy-based routing, memory management, and program-level lifecycle observability
- Define RAG pipeline standards across engagements: establish chunking and embedding strategies, set quality benchmarks, and ensure metric-backed tradeoff decisions are documented and transferable
- Set multi-LLM integration standards: vendor-agnostic architecture by default, fallback routing and cost governance as standard design practice across providers including OpenAI, Anthropic, Vertex AI, and open-source models
- Own LLM Ops at program scale: eval strategy, prompt governance, observability tooling standards, safety monitoring and cost controls across multiple concurrent systems
- Lead client development engagements at senior level — facilitate architecture design sessions, lead proof-of-concept delivery, and drive alignment between client technology leadership and delivery teams
- Shape and publish reusable patterns, accelerators, and development standards that scale across the practice and reduce ramp-up time on new client engagements
- Own the measurement framework for agentic system quality: define accuracy, latency, safety, and cost metrics; present program-level AI impact in business terms to senior client stakeholders
Basic Qualifications
Required qualifications
- Bachelor’s degree or college diploma in a related field.
- 10+ 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 such as 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 considerations
- Experience building and operating RAG pipelines, including embeddings, chunking strategies, vector databases, and context design
- Experience with LLM Ops practices, including eval harness design, prompt versioning, and production observability
- Experience with cloud-native development, including Kubernetes, Docker, microservices, serverless, 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
- 10 years of experience leading software development teams, including delivery oversight, workstream allocation, and development of team members
- Experience defining and governing enterprise-scale agentic architectures across multiple systems
- Experience setting standards for RAG pipelines, LLM integration, or LLM Ops practices across multiple deployments
- Experience leading client-facing technical engagements, including architecture sessions or proof-of-concept delivery
- Experience defining and measuring AI system performance metrics (accuracy, latency, safety, cost)
- 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 $142,050 to $292,000
#LI-NA-FY25 and #LI-MP
Toronto, Ontario
Calgary, Alberta
Montreal, Quebec
Ottawa, Ontario
Vancouver, British Columbia
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