Head of Engineering - Technology, Media & Telecom

Fractal Analytics

1d•$300,000

About The Position

Fractal is a strategic AI partner to Fortune 500 companies, with a bold vision: to power every human decision in the enterprise. We believe the future belongs to organizations that combine human imagination with intelligent systems—and Fractalites are the ones building that future. As we scale our Technology, Media & Telecom (TMT) practice in the United States, we are looking for a senior, client-facing Head of Engineering to shape and deliver world-class Data & AI platforms for leading Technology, Media & Telecom organizations. This is not a back-office engineering role. This is a consulting-led, client-facing engineering leadership position for someone who is equally comfortable whiteboarding architecture with principal engineers, rolling up their sleeves with delivery teams, and advising CIOs, CTOs, and CDOs in the boardroom. Learn more at Fractal | Intelligence for Imagination. Location: San Francisco, CA / Bay Area Note: This position is not eligible for Immigration Sponsorship at this time. About the Role This is a four-axis leadership role requiring technical depth, executive presence, team leadership, and embedded delivery. You'll work directly with top technical and functional leaders at some of the largest TMT companies in the world. As Head of Engineering for Fractal's Technology, Media & Telecom (TMT) vertical, you will personally shape the architecture of mission-critical AIML platforms, often in first-party tech stack, and develop/drive the team of ICs who bring them to life.

Requirements

15–20 years of experience spanning AI/data engineering and technical leadership with clear evidence of owning architecture at scale
Deep hands-on experience deploying AI/ML/GenAI systems in production, in addition to advising on them
Demonstrated executive presence: you have walked into a CTO or CDO review, redrawn the architecture based on new constraints, and left the room with alignment
The ability to whiteboard fluently under pressure, synthesizing a team's in-flight work with a new business direction, making it rigorous enough for engineers and clear enough for executives, on the spot and without a rehearsal
Experience operating within client-owned or non-standard technology stacks - you have learned a proprietary system, earned trust from skeptical internal engineers, and delivered production-grade results inside someone else's infrastructure
A track record of leading senior engineers and building high-performance ML/engineering teams, including hiring, coaching, and developing principal-level ICs
Direct executive engagement experience - you have influenced CIO/CTO/CDO decisions and can hold your own in a room with technical and non-technical stakeholders at once
Strong cloud-native fluency across one or more hyperscalers, with genuine depth in data platform patterns (streaming, batch, Lakehouse, governance)

Nice To Haves

Experience in TMT vertical — hi-tech, telco, media platforms, streaming infrastructure, ad tech, or content delivery at scale
Prior work in FDE-style or embedded delivery models where your team shipped inside a client codebase and was evaluated by their engineering standards, not just deliverable milestones
Comfort with the ambiguity of 1P stack environments: you have debugged undocumented internal tools, extended proprietary frameworks, and figured out how to make external expertise land inside a closed ecosystem
A personal reputation for architectural clarity: the person colleagues call when a problem needs to be drawn, not just describe
Contributions to the ML/AI community: open source, publications, conference talks, or influential architectural patterns

Responsibilities

Own AI/Data platform architecture decisions — from Lakehouse design and real-time streaming to MLOps, LLMOps, and AgentOps pipelines in production
Serve as the technical authority for Fractal's TMT engineering practice — defining standards, reviewing design, and holding the bar on reliability, scalability, and security
Translate ambiguous business problems into concrete, buildable platform architectures — and stay close enough to execution to know when something is not working
Drive the industrialization of GenAI: moving clients from proof-of-concept to enterprise-grade, governed, and observable AI systems
Command the room with senior client leadership — CIOs, CTOs, CDOs, and their direct reports - as a peer, not a vendor
Whiteboard new architectural directions on the spot: when a business pivot, acquisition, regulatory shift, or technology breakthrough forces a mid-execution rethink, you synthesize it into a credible, buildable path forward live, in the room, without needing a week to prepare a deck
Translate between two worlds simultaneously: make the architecture legible to a CFO and rigorous enough to satisfy a principal engineer in the same session
Shape client roadmaps at the strategic level; identifying where the current plan is under-ambitious, over-engineered, or misaligned with emerging AI capabilities, and steering accordingly
Represent Fractal at the highest level of client relationship
Develop and lead a high-performing group of individual contributors. principally senior and staff engineers, ML engineers, and data platform engineers
Create the engineering culture: rigorous delivery standards, architectural thinking, and a bias toward elegant, production-grade solutions over quick fixes
Build leadership depth within the team, identifying principals who can own programs and grow into broader roles
Partner across Fractal's global AI and engineering Capability functions to staff programs strategically and raise capability across the TMT practice
Lead and run FDE-style engagements where your team operates inside the client's engineering environment
Navigate and deliver within client-owned, first-party technology stacks: proprietary data platforms, internal ML infrastructure, custom orchestration systems, and bespoke toolchains that do not appear in any industry survey
Adapt quickly to non-standard environments, understanding a client's internal platform deeply enough to extend it, integrate into it, and earn the trust of their engineering staff
Balance the tension between what Fractal does best and what the client's stack demands, knowing when to bring pattern, when to adapt, and when to advocate for a better path
Set the standards for how Fractal operates in deeply embedded engagements: how we onboard, document, transfer knowledge, and leave clients stronger than we found them