Head of AI Enablement & Software Engineering

Job Title: Head of AI Enablement & Software Engineering

Reports To: Operating Partner, AI C Technology Enablement

Role Type: Permanent, Individual Contributor (Portfolio-Wide, High Visibility)

Location: Hybrid in New York or San Francisco

Travel to SF every 6 weeks. Travel to India 1-2 times per year

About The Company

This is a leading U.S.-based private investment firm, with more than 180 employees and offices across San Francisco, New York, Dallas, Chicago, Greenwich, Scottsdale, and London. The firm has raised over $47 billion in capital and invests on behalf of leading institutional investors worldwide through three tightly aligned strategies: Private Equity, Real Estate, and Data Infrastructure. They are known for pairing deep sector specialization with hands-on operational engagement, backing market-leading businesses that sit at the intersection of technology, data, and mission-critical services.

At the core of their investment thesis is a focus on software, SaaS, digital infrastructure, and data centers, the foundational platforms powering the modern digital economy and the fourth industrial revolution. The Private Equity strategy concentrates on high-growth companies in software, healthcare, and services, with a strong emphasis on SaaS platforms where technology-driven value creation can be accelerated. The Data Infrastructure strategy invests in hard-asset infrastructure, including data centers and digital backbone assets, that enable cloud computing, AI, and enterprise digitization at scale.

Complementing this, the Real Estate strategy targets specialized properties supporting technology, life sciences, and healthcare. This executive opportunity reflects their continued commitment to technology-led value creation, seeking a Principal Technologist to lead and enable agentic AI and advanced AI capabilities across the portfolio, translating innovation into durable operational and financial impact.

Position Overview

This is a strategic hire that will be responsible for strategy, value creation, and measurable AI adoption inside workflows across ~12–15 SaaS portfolio companies. This is an amplifier role that requires a technically fluent AI practitioner who understands how developers work today (“vibe coding,” copilots, agents, PR-driven development) and can safely move teams from assistive AI (review/edit) toward agentic workflows that materially improve productivity, quality, and development velocity. You will work hands-on with product, engineering, QA, Product, and DevOps leaders to deploy tools, patterns, and set guardrails that still scale, while creating repeatable playbooks the firm can apply across current investments and future acquisitions.

Job Responsibilities

Lead the firm’s portfolio-wide AI strategy and execution, driving the adoption of agentic and advanced AI capabilities to accelerate product and value creation. This role defines the AI roadmap, establishes scalable data and AI foundations, advises and strategizes on interoperable infrastructure, and partners with portfolio companies CTO, CPO, and appointed AI leads to embed AI into core workflows and decision-making across the business.

Working closely with operating partners, the leader will prioritize high-impact use cases, measure ROI, and ensure AI initiatives move from pilots to production within the SDLC, increasing overall development velocity across the fund. The role owns the AI operating model across the fund, including talent, vendors, governance, enabling repeatable and scalable AI deployment across portcos and conducing diligence on future acquisitions.

The ideal candidate is a senior business and technology leader with the ability to translate AI capabilities into financial impact, influence executive stakeholders, and lead technical delivery in complex environments. Must understand finance and/or Private Equity, DevOps, AI and modern development

techniques.

Percentage Of Experience

* 50% Technology & AI

* 25% Infrastructure / Engineering

* 25% Agentic and GenAI Capabilities

* 30% Analytics

* Reporting / Metrics

* Driving efficiency, effectiveness, and profitability through AI/Technology

* 20% Business

* Use case for value creation

* Communication with technical leaders at each portco to roadmap AI Strategy

* Communication with internal business leaders at the firm to display success through measurable metrics

AI Enablement Requirements

* Lead AI enablement across the end-to-end developer experience (product, engineering, QA, CI/CD, operations).

* Embed AI directly into day-to-day workflows, not slideware, not labs, but driving real measurable adoption inside IDEs, repos, pipelines, and PRs.

* Serve as a trusted technical partner to CTOs, CPOs, VPs of Engineering, and platform leaders across the portfolio.

SDLC Fluency & AI-in-SDLC Enablement Track Record

* Deep understanding of how software is built/shipped (requirements → coding → testing → CI/CD → release → ops) and where cycle time + quality break.

* Has deployed AI tooling/workflows into engineering orgs and driven adoption beyond pilots, with measured outcomes and repeatable rollout motions. Includes experience building and deploying autonomous, orchestrated agents and enabling agentic workflows.

Agent Workflows “Vibe Coding” Experience

* Design, deploy, and orchestrate agentic workflows.

* Mobilize AI agents with human-in-the-loop controls, expanding autonomy by risk tier and service criticality.

* Help teams evolve from prompt-based usage into durable, auditable agent workflows that ship code and increase velocity.

* Enough technical depth to evaluate AI tools, limitations, integration patterns, and what’s “real” vs hype (without needing to be hands-on keyboard day-to-day)

Change Managements Enablement

* Build repeatable AI enablement playbooks: role-based learning paths, internal champion, networks, sprint rituals, feedback loops, and portfolio guardrails.

* Design scalable enablement programs that work across U.S. and India delivery models.

* Create employee artifacts that teams can reuse: IDE configs, CI templates, agent patterns, training modules, and governance standards.

* Establish a baseline for reinforcement learning and human feedback (RLHF) for proprietary LLM’s and post deployment of AI Agents to ensure quality output.

Governance, Risk, And Compliance

* Embed security and compliance by design throughout the build: IP protection, data boundaries, secrets management, model controls, and auditability.

* Evaluate evolving vendor risk postures and trust centers; adjust controls as AI features and capabilities change.

* Partner with portfolio security leaders to ensure AI adoption strengthens and evaluate enterprise risk posture.

* Can design and implement pragmatic governance that enables speed while managing risk across different portco contexts.

Vendor Management Procurement

* Evaluate, select, and negotiate AI tooling and enablement vendors across the portfolio.

* Reduce fragmentation, improve leverage, and drive favorable portfolio-wide economics.

* Make pragmatic build vs. buy vs. partner decisions based on company maturity and value- creation priorities.

Reporting Structure

* Establish baselines and track outcomes such as:

* PR cycle time and throughput

* DORA metrics

* Defect escape rates

* Developer adoption and engagement

* Drive ORK or similar frameworks to keep deliverables on track

* Share cross-portfolio benchmarks and success patterns with leadership.

Requirements (Must-Haves)

* 3+ years creating/owning end-to-end modern AI/Data strategy for an organization (executive level); strong understanding of AI tools and use cases.

* Excellent stakeholder management and communication; able to partner with senior leaders and cross-functional teams as the “translator” between business needs and technical execution.

* Strong understanding of APIs, Integrations, and MCP Architectures

* Strong business acumen, especially comfort with accounting/finance fundamentals and how operational data maps to financial “scorekeeping”.

* Understanding and ability to implement quantitative metrics to track project success and scalability.

* Experience successfully deploying agentic AI capabilities for software development groups/organizations.

* Experience evaluating, consolidating, and managing various vendor relationships.

* Strong understanding of necessary security measures, data governance, and compliance necessary to effectively implement AI tools.

* Ability and proven experience implementing effective change management, training, and enablement across organizations.

10+ years of overall experience in the AI/Data/SW Development space.