VP, Engineering

TechnoMile•McLean, VA

17h•Hybrid

About The Position

TechnoMile is seeking a VP, Engineering to lead the Engineering organization and turn the product roadmap into shipped, high-quality software. Reporting to the Chief Product and Technology Officer, this leadership-first role is accountable for Product Engineering, Platform, QA, DevOps, and Cloud Operations across teams in the United States and India. The VP will build and grow the organization, establish operational discipline, and drive consistent execution while making sound technical and architectural tradeoffs as needed. The role partners closely with Product and company leadership to prioritize the roadmap, translate business needs into clear execution plans, and communicate progress, risks, and tradeoffs to executives. The VP will own delivery predictability, headcount planning, recruiting strategy, and the development of engineering managers and senior individual contributors. They will also identify opportunities to use AI in engineering workflows to improve productivity, quality, and efficiency. The position is open to candidates in the Washington, DC metro area and is hybrid, reporting to the Tysons Corner, Virginia office, typically two to three days onsite as needed. The Engineering team spans the U.S. and India and is responsible for building, testing, and operating TechnoMile’s AI-enabled product suite.

Requirements

15+ years of experience in software engineering.
5+ years in a senior engineering leadership role managing managers across multiple teams, ideally including distributed or offshore teams.
Demonstrated ability to build, grow, and retain strong engineering teams, with a focus on developing people.
Strong command of engineering processes and delivery methodologies such as Agile or Kanban.
Experience establishing planning cadences, metrics, and accountability across teams.
Proven ability to prioritize a roadmap against business goals and drive completion across multiple teams and time zones.
Strong engineering background in full-stack development and cloud-native architecture sufficient to earn the team’s trust and make sound technical tradeoffs.
Excellent verbal and written communication skills, including the ability to represent Engineering credibly to executive leadership.
Hands-on experience using generative AI and automation tools to improve productivity, and sound judgment about when to use AI versus human insight.

Nice To Haves

Experience leading engineering organizations split across the U.S. and India or other global delivery centers.
Experience in a B2B SaaS company, ideally serving the GovCon, federal, or regulated enterprise market.
Experience with Azure DevOps (ADO), Jira, or similar delivery-tracking platforms.

Responsibilities

Lead, grow, and develop the Engineering organization, including managers and technical leads across U.S. and India teams.
Prioritize and align the roadmap with Product and company leadership, translating business priorities into sequenced execution plans.
Drive execution across distributed teams and ensure work is scoped, tracked, and delivered against commitments.
Establish and enforce engineering processes, including planning cadences, delivery metrics, definitions of done, and accountability for outcomes.
Own delivery predictability by tracking release cadence and on-time delivery and surfacing timeline risks.
Identify and resolve execution bottlenecks, partnering with managers to unblock teams.
Coach and develop engineering managers and senior individual contributors and build clear career paths and a strong leadership bench.
Own Engineering headcount planning and recruiting strategy in partnership with People and Talent Acquisition.
Represent Engineering to executive leadership with clear, data-driven updates on roadmap progress, risks, and tradeoffs.
Make technical and architectural tradeoffs and guide the team’s technical direction when needed.
Partner with Product and Customer Delivery to ensure engineering priorities reflect customer and business needs.
Oversee Platform, QA, DevOps, and Cloud Operations to support predictable end-to-end delivery.
Identify opportunities to embed AI into engineering workflows to improve velocity, quality, and team efficiency.