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Accenture

Advises businesses and runs their technology

AI Solution Engineering Senior Manager - CG&S

Engineering LeadershipPosted on Nov 13
Full-time

About the Job

Requirements

  • At least 10 years of experience in technology, data, or AI roles across consulting or industry, with experience across varied industries and business problems.
  • At least 5 years leading technology, data, or AI programs end to end, including managing complexity, ambiguity, shifting scope, and senior stakeholders.
  • Hands-on experience moving AI products into production, including retrieval-augmented generation (RAG), agentic workflows, evaluations, and guardrails.
  • Working fluency with the Forward Deployed Engineering technical stack, including large language model APIs across frontier and open-weight models such as Claude, Gemini, GPT, Llama, and Mistral; agentic frameworks and Model Context Protocol (MCP); RAG pipelines and vector databases; Python and TypeScript; REST and GraphQL integration; and model and prompt evaluations.
  • Production delivery experience with at least one enterprise agent platform, such as Amazon Bedrock, Microsoft Azure, Google Gemini, or Databricks Mosaic.
  • At least 5 years directly managing and developing technical practitioners and demonstrated ability to grow them into workstream and program leaders.
  • At least 5 years leading transformation in customer service, contact center, or customer experience operations, with a working understanding of service metrics including containment, first-contact resolution, average handle time, customer satisfaction, and cost-to-serve, and how AI affects them.

Responsibilities

  • Oversee AI product engineering programs, providing technical direction and quality oversight across leads and their teams while maintaining accountability for delivery health, team performance, and measurable outcomes such as resolution, average handle time, customer satisfaction, and adoption.
  • Stay connected to delivery by inspiring and challenging team designs, unblocking stalled work, and giving clients a candid view of what it takes to put AI into production.
  • Direct how Forward Deployed Engineers embed with clients: run discovery to identify high-value use cases, prototype quickly to prove value, and own solutions end to end through production rollout, adoption, runbooks, and a clean handoff to client or managed-delivery teams.
  • Lead delivery of production AI applications for service, including conversational AI agents across voice and digital channels, human agent-assist copilots, knowledge assistants, interaction summarization and quality automation, and analytics agents that let leaders query service data in plain language.
  • Hold Forward Deployed Engineers to production-grade standards, including tested Python and TypeScript services, evaluation suites that catch regressions, observability, and clear cost, latency, and safety tradeoffs, so solutions run reliably after handoff.
  • Set engineering standards for how AI components work together, including RAG design, prompt and context engineering, evaluations and guardrails, model selection, and deciding when fine-tuning merits the investment.
  • Originate use cases with clients, prioritize by measurable value, and shape engagements that build toward a real product rather than a proof of concept.
  • Turn conversation designs, customer experience journeys, and prototypes into production code; co-create from early concepts and feed insights from data and model behavior back into the experience.
  • Ensure shipped solutions are secure, monitored, and responsible by partnering with specialists in MLOps, AI security, and responsible AI.
  • Grow a pipeline of work by building client relationships, connecting Song's service AI capabilities to client priorities, and crafting proposals.
  • Use Forward Deployed Engineer-led discovery and rapid prototypes to prove value before full engagement, and partner with sales teams on business cases, return-on-investment models, executive narratives, and request-for-proposal responses.
  • Develop thought leadership on service AI products that matter to clients, including agentic customer service and analytics agents, and advance Song Service as an industry voice.
  • Strengthen relationships with AI and service platform partners to improve joint solutions and go-to-market efforts.
  • Lead, coach, and grow Forward Deployed Engineers and surrounding engineering teams, helping practitioners develop technical depth, leadership skills, and client acumen so they can become workstream and program owners.
  • Build inclusive, high-performing teams where practitioners are supported, challenged, and able to develop into stronger leaders.
  • Scale the Forward Deployed Engineering capability within Service AI & Data by defining client-engagement approaches and building career paths from FDE to workstream and program leadership.
  • Bring AI-augmented delivery into teams by applying AI tooling to use-case discovery and prioritization, story grooming, solutioning, testing and triage, and development to improve speed and quality.
  • Codify delivery patterns, evaluation suites, and reusable components, and feed field learnings back into offerings to make delivery repeatable.

Desired Qualifications

  • A product-led mindset that connects AI and data work to product strategy and the design-to-product pipeline.
  • Experience leading production delivery of conversational AI, such as virtual agents, voice bots, agent-assist copilots, and knowledge assistants, spanning conversation design, dialog and intent management, and LLM-based agents across voice and digital channels.
  • Experience embedding AI into contact center and customer relationship management platforms such as Salesforce Service Cloud, ServiceNow, Microsoft Dynamics 365, Genesys Cloud, NICE CXone, or Amazon Connect, and into everyday service-agent workflows.
  • Experience with conversation intelligence, such as speech and interaction analytics, sentiment, and automated quality assurance, turning service conversations into enterprise-level insight.
  • A track record of leading business development by shaping proposals, scoping programs, and presenting to clients to build confidence and close deals.
  • Executive presence and ability to hold difficult conversations with senior client stakeholders, guiding them toward outcomes that strengthen relationships while setting expectations that protect delivery teams.
  • Demonstrated ability to connect technical decisions to business outcomes and influence cross-functional decision-making.
  • Experience with conversational AI and agent platforms such as Google Customer Engagement Suite, Amazon Lex, Microsoft Dynamics 365 Contact Center, Salesforce Agentforce, or ServiceNow AI Agents.
  • A full-stack engineering background in web, mobile, or API development.
  • Experience working in multidisciplinary teams spanning design, advisory, and technology, turning research insights and design recommendations into engineered solutions.
  • Experience working in management consulting or a professional services environment.
  • Contribution to external thought leadership, such as publishing points of view, speaking at events, or representing the practice in the broader AI community.

About the Company

Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1989

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