PreSales Collective
Solutions Engineer, Enterprise
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Solutions Engineer, Enterprise
Solutions Engineer – AI & Generative AI Solutions
Location: [Assumed: Global/Hybrid as implied] Industry: AI, Enterprise SaaS, Generative AI, LLMs Team: Scale AI – Pre-Sales & Solutions Engineering
About the Role
At Scale, we’re empowering the world’s most innovative companies to build reliable AI systems for critical decisions. As a Solutions Engineer, you’ll play a pivotal role in accelerating enterprise adoption of our AI solutions. You’ll partner with Account Executives (AEs), Product, and Machine Learning Engineers (MLEs) to lead pre-sales engagements, deliver customized demos and pilots, and win over tech-savvy customers. This is a hands-on, high-impact role where technical expertise, business acumen, and collaboration drive real outcomes.
Your work will directly impact how enterprises in financial services, insurance, SaaS, and beyond leverage Generative AI to transform their operations. We’re looking for technically strong, empathetic problem-solvers who thrive at the intersection of sales, engineering, and customer success.
Key Responsibilities
Your core focus will be to secure the technical win while navigating complex enterprise needs. Daily tasks include:
Customer Engagement & Solution Design
- Act as a trusted advisor to prospective customers, guiding them through prototype development, PoCs, and pilots tailored to their business needs.
- Translate abstract AI/ML requirements into actionable, scalable solutions using Scale’s GenAI and data infrastructure capabilities.
- Collaborate with AEs, Product Managers, and Engineering to refine and iterate on solutions throughout the sales cycle.
- Closely monitor technical challenges that could derail a deal, proactively unblocking objections with expert insights.
- Present complex technical solutions to executives, engineers, and non-technical stakeholders—convincingly and persuasively.
Technical Collaboration & Implementation
- Scope customer technical requirements into detailed Scopes of Work (SOWs), breaking down pain points into executable project plans.
- Work cross-functionally with development teams (Software Engineers, MLEs) to define, build, and deploy agents during the post-sales onboarding phase.
- Identify customer-specific feature requests and prioritise them alongside the AE and PM teams.
- Advise on architecture best practices for integrating Scale’s technologies into enterprise workflows.
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Team & Process Improvement
- Drive initiatives to enhance the Solutions Engineering team’s efficiency, such as:
- Standardizing conflict resolution for technical sales challenges.
- Implementing mechanisms to reduce friction between sales and engineering.
- Mentor other team members on AI-driven solution strategies and customer advocacy techniques.
What You’ll Bring
Required
- A strong engineering background, with experience working with clients in pre- or post-sales roles (e.g., Solutions Engineer, OKR Lead, or similar).
- Hands-on experience with Python, Java, or other web development languages, demonstrating the ability to:
- Write technical memorandums (TMs) and explain complex systems clearly.
- Provide proof-of-concept work during engagements.
- Experience in one or more of these domains:
- Enterprise SaaS, cloud technologies, or fintech; working with end customers in a technical capacity.
- Generative AI (LLM applications), preferably in enterprise contexts.
- Self-starter mentality: Proven ability to:
- Unblock technical challenges independently while “in the field” (away from HQ).
- Adjust quickly in high-pressure environments (e.g., competitive bid scenarios).
- Technical storytelling skills:
- Ability to connect with executives (without drowning them in jargon).
- Comfort holding engaging, 30–60 minute technical interactions with teams in challenging sights.
- Cross-functional collaboration:
- Strong AEs who can speak the tech sales language.
- Tailor communication to diverse stakeholders (from CxOs to developers).
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Nice To Haves
- Experience with large language model deployments or generative AI use cases in high-impact industries.
- Background as a forward-deployed engineer, working closely with clients to operationalise technical solutions.
- Prior Machine Learning/ML Engineer experience (full-stack GenAI systems, data pipelines, etc.).
- Exposure to data infrastructure or AI operations challenges in large-scale enterprises.
Why This Matters
Enterprise AI adoption is a collaborative sport. Your ability to work with AEs to co-pilot a deal from original contact to contract signing, align technical maturity with business goals, and unblock roadblocks with empathy will shape how Scale scales. This is not a solo endeavour; we value teamwork and mutual growth.
About Scale
We build the reliable foundation for world-changing AI systems. Our tech powers deployment in:
- Finance (e.g., JPMorgan, Wells Fargo)
- Healthcare (e.g., Mayo Clinic)
- Defence (U.S. Army, Air Force)
- Media (e.g., Time Inc.)
- Enterprise (Meta, Ernst & Young, Qatar Government)
Our mission is to democratise AI’s benefits across industries—and we need ** solvers** like you to redefine what’s possible.
Our Commitment to Inclusion
At Scale, we aim to be an inclusive employer where everyone—regardless of gender, race, ability, experience—feels valued. We stay transparent about compensation (Pay Transparency Compliance).
We accommodate disabilities in hiring: If you need adjustments to navigate our process, email accommodations@scale.com.
We also share applications with affiliates: By applying, you consent to our privacy practices (as outlined below).
Policies & Next Steps
Disclaimer: There is a 90-day forfeiture period for resumes recycling—we take candidate development seriously.
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