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Accenture

Advises businesses and runs their technology

AI Platform Engineering Consultant

Platform EngineeringPosted on Nov 13
Full-time

About the Job

Requirements

  • At least 3 years of professional experience in platform, cloud, DevOps, site reliability, or data engineering, or in a client-facing technical role such as technical consulting, with hands-on experience deploying and operating AI or large language model-powered applications in production, ideally integrated with enterprise systems rather than limited to prototypes or demos.
  • At least 1 year of experience working on customer service, contact center, or customer experience projects or operations, with understanding of relevant metrics such as resolution, average handle time, and customer satisfaction score.
  • Hands-on experience with at least one cloud platform (AWS, Azure, or Google Cloud), containers, Kubernetes, infrastructure as code such as Terraform, and continuous integration and continuous delivery.
  • Programming and scripting skills in Python, plus SQL for querying data and validating pipelines.
  • Experience deploying and running AI or machine learning workloads, such as model serving and inference, on managed AI platforms such as Amazon Bedrock, Microsoft Azure AI Foundry, Google Vertex AI, or Databricks Mosaic AI.
  • Experience with observability and performance tooling such as OpenTelemetry, Grafana, or Datadog, and with load testing.
  • Experience with modern data platforms such as Databricks, Snowflake, BigQuery, or Microsoft Fabric.
  • Ability to translate technical work into clear business value and communicate with developers and client leaders.
  • Willingness to travel regularly to client locations.

Responsibilities

  • Work embedded with client technology teams, on site and remotely, to understand the platforms, data, and operational constraints behind their service operations, including contact center and telephony systems and cloud environments.
  • Partner with Forward Deployed Engineers and AI Solution Engineering teams to take prototypes to production by providing the required environments and platform tooling from day one.
  • Stand up environments, model-serving and inference endpoints, and deployment pipelines quickly so prototypes have a clear path to production without a rebuild.
  • Build and maintain AI platform infrastructure using infrastructure as code, containers, and continuous integration and continuous delivery across cloud and hybrid environments.
  • Run performance and load testing for real-time voice and chat workloads, tune latency, and help plan capacity for seasonal and event-driven peaks.
  • Track and optimize the cost of AI at scale through token usage, model routing, caching, and right-sizing, and report it in a way client teams can act on.
  • Build the data pipelines and platform components behind service data lakes, reporting, dashboards, and analytics agents.
  • Set up observability, logging, and alerting; monitor against service-level objectives for performance and reliability; respond to incidents; and write runbooks for client teams to use after handoff.
  • Bring conversation designs, customer experience journeys, and prototypes to life in code, flag technical constraints early, and iterate as designs evolve and real usage data becomes available.
  • Support production rollout and adoption by writing runbooks, training client teams on new AI-powered workflows, and helping deliver a clean handoff to client or managed-delivery teams.
  • Use AI-assisted engineering tools across story grooming, solutioning, testing and triage, and development to improve delivery speed and quality.
  • Contribute reusable components, accelerators, and field lessons so teams can deliver faster on future engagements.
  • Help onboard and mentor junior teammates, and contribute to project debriefs and team points of view.

Desired Qualifications

  • Exposure to contact center platforms such as Amazon Connect, Genesys Cloud, or NICE CXone, including voice and telephony infrastructure (SIP, WebRTC) or real-time speech services such as speech-to-text and text-to-speech.
  • Experience with interaction data such as calls, chats, and transcripts, or workforce and quality data.
  • Associate-level cloud certifications or FinOps certification.
  • A portfolio of real-world AI projects or deployed solutions that can be discussed.
  • Experience with client-facing projects, ideally in consulting, agency, or other professional services.
  • Bachelor's degree in computer science, engineering, or a related field, or equivalent work experience.

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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