About Quantiphi:
Quantiphi is an award-winning, AI-First digital engineering and consulting company focused on delivering high-impact Services and Solutions that help organizations solve what truly matters. We partner with enterprises to reimagine their businesses through intelligent, scalable, and transformative AI—driving measurable outcomes at the very core of their operations.
Since our founding in 2013, Quantiphi has tackled some of the world’s most complex business challenges by combining deep industry expertise, disciplined cloud and data engineering practices, and cutting-edge applied AI research. Our work is rooted in delivering accelerated, quantifiable business value—not just technology for technology’s sake.
Headquartered in Boston, Quantiphi is a global organization with 4,000+ professionals serving clients across key industry verticals, including BFSI, Healthcare & Life Sciences, CPG, MFG, TME etc. As an Elite and Premier partner to leading cloud and AI platforms such as NVIDIA, Google Cloud, AWS, and Snowflake, we build and deliver enterprise-grade AI services and solutions that create real-world impact.
Role: Engagement Manager - CCAI
Experience Level: 7+ years
Location: Remote (UK)
Role Summary:
To maximize the ROI of our high-value engineering talent, we require a Technical Program Manager to operate in a high-intensity delivery environment. You will act as a force multiplier, removing logistical friction (travel, access rights, scheduling) to ensure our FDEs remain 100% focused on coding and customer delivery. You will bridge the gap between field challenges and core engineering requirements.
Example of Work Required:
* Triage issue reports from customers, identify likely root causes, and identify owners on Google/customer team to continue investigation.
* Build organization around issues/feature requests, manage ETAs for fixes and improvements for Google-owned issues.
Key Responsibilities:
* Issue Triage & Root Cause Analysis: Triage issue reports from customers, identify likely root causes, and assign owners within Google or customer teams. Build organization around issues/feature requests and manage ETAs for fixes.
* Operationalizing Scale: Enforce strict \"train-the-trainer\" certification milestones and cohort velocity to scale the team from 8 to 24+ FTEs, preventing quality dilution during rapid expansion.
* Systematizing Product Feedback: Transform isolated field challenges into prioritized engineering requirements. Aggregate distributed data on ADK/NGA edge cases to drive roadmap evolution.
* Logistical Friction Removal: Remediate transparency of work vertically and horizontally, ensuring FDEs are not bogged down by the logistical friction of handling customer messaging.
Core Competencies:
* Technical Fluency: \"Code comfortable\" with the ability to read Python and understand NGA app architecture.
* Operational Rigor: Ability to manage high-velocity scaling (1:3 ratio governance).
* Strategic Communication: Centralizing messaging to maintain alignment between Google Engineering and the customer.
Minimum Qualifications:
* Technical Understanding: Ability to read Python and understand complex software architectures.
* Infrastructure Knowledge: Professional-level certification in Google Cloud Infrastructure.
* Agent Experience: Understanding of LLM agents, including prompt engineering and core ADK/NGA concepts (instructions, callbacks, tools).
Preferred Qualifications:
* Leadership Experience: Experience operating at an L7+ level equivalent to shape the practice and make staffing adjustments.
* Hands-on Background: Previous hands-on delivery experience, preferably in an FDE-type role, to better understand the technical nuances of the program.
What is in it for you:
* Be part of the fastest-growing AI-first digital transformation and engineering company in the world.
* Be a leader of an energetic team of highly dynamic and talented individuals.
* Exposure to working with fortune 500 companies and innovative market disruptors.
* Exposure to the latest technologies related to artificial intelligence and machine learning, data and cloud.