You bring a reliability mindset to everything you build: you plan for peak demand, measure what matters, and make sure systems keep running long after go-live. You are comfortable in fast-moving, ambiguous environments, moving between engagements, picking up new platforms quickly, and explaining technical tradeoffs in plain language to engineers and business stakeholders alike. Above all, you are a curious, collaborative teammate who makes the people around you better.
The work
Work embedded with client technology teams, on site and remote, to understand the platforms, data, and operational constraints behind their service operation, from contact center and telephony systems to cloud environments.
Partner with Forward Deployed Engineers and AI Solution Engineering teams to take prototypes to production, giving them the environments and platform tooling they need 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 CI/CD 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 performance and reliability targets (SLOs), respond to incidents, and write the runbooks client teams use after handoff.
Bring conversation designs, customer experience journeys, and prototypes to life in code, flagging technical constraints early and iterating quickly as designs evolve and as real usage data comes in.
Support production rollout and adoption: write runbooks, train client teams on new AI-powered workflows, and help deliver a clean handoff to client or managed-delivery teams.
Use AI-assisted engineering tools every day across story grooming, solutioning, testing and triage, and development to raise the speed and quality of delivery.
Contribute reusable components, accelerators, and lessons learned from the field so our teams deliver faster on the next engagement.
Help onboard and mentor junior teammates, and contribute to project debriefs and team POVs.
ere's what you'll need
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 LLM-powered applications in production, ideally integrated with real enterprise systems rather than prototypes or demos only.
1+ years of experience working on customer service, contact center, or customer experience projects or operations, with an understanding of the metrics that matter there, such as resolution, average handle time, and CSAT. Experience should include:
Hands-on experience with at least one cloud platform (AWS, Azure, or Google Cloud), including containers and Kubernetes, infrastructure as code such as Terraform, and CI/CD.
Programming and scripting skills in Python, plus SQL for querying data and validating pipelines.
Experience deploying and running AI or ML 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.
The ability to translate technical work into clear business value, and to communicate with everyone from developers to client leaders.
Willingness to travel regularly to client locations.
Bonus points if you have
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 (speech-to-text and text-to-speech).
Experience with interaction data such as calls, chats, and transcripts, or with workforce and quality data.
Associate-level cloud certifications or FinOps certification.
A portfolio of real-world AI projects or deployed solutions you can walk us through.
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.
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 for roles that may be hired as set forth below.
We anticipate this job posting will be posted until 1/31/2027.
Accenture offers a market competitive suite of benefits including medical, dental, vision, life, and long-term disability coverage, a 401(k) plan, bonus opportunities, paid holidays, and paid time off. See more information on our benefits here:
U.S. Employee Benefits | Accenture
Role Location Annual Salary Range
California $70,350 to $205,800
Colorado $63,800 to $177,800
Connecticut $63,800 to $177,800
District of Columbia $68,000 to $189,300
Illinois $59,100 to $177,800
Maine $54,400 to $151,400
Maryland $63,800 to $177,800
Massachusetts $63,800 to $189,300
Minnesota $63,800 to $177,800
New York $66,300 to $205,800
New Jersey $68,000 to $205,800
Ohio $59,100 to $164,600
Virginia $59,100 to $189,300
Washington $80,200 to $189,300
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