At ShyftLabs, we live and breathe data. Since 2020, we’ve been helping Fortune 500 companies unlock growth with cutting-edge digital solutions that transform industries and create measurable business impact. We’re growing fast and we’re looking for passionate problem-solvers who are ready to turn big ideas into real outcomes.

The Opportunity

We’re looking for a Sales Engineer (AI & Data Platforms) to bridge the gap between enterprise data, AI capabilities, and real-world business outcomes. This is not a traditional pre-sales role, it is a hybrid technical and commercial position focused on turning complex ideas into working systems that drive measurable impact.

At ShyftLabs, we don’t sell decks or theoretical AI. We partner with organizations that already have data and models but struggle to operationalize them. Your role will be to translate ambiguous business problems into applied AI systems, rapidly prototype solutions, and build trust with stakeholders that these systems will work in their environment.

You will operate at the intersection of solution architecture, prototyping, and deal execution, working closely with leadership, clients, and engineering teams to move AI from experimentation to production.

This role is best suited for a candidate with a strong technical foundation in data and AI systems, a builder mindset, and the ability to connect technical decisions directly to business value.

• Own the technical narrative in enterprise deals: Translate ambiguous client challenges into structured problem definitions and solution architectures grounded in real-world constraints such as data availability, infrastructure, and governance. Clearly articulate why proposed solutions will succeed and where alternative approaches fall short.

• Turn conversations into working systems (fast): Build prototypes, POCs, and demos using real or representative data to validate feasibility and demonstrate impact. Integrate AI into existing workflows, showcasing decision systems, automation layers, and agent-driven processes rather than isolated models.

• Make AI real for clients: Shift discussions away from theoretical models toward practical applications, including decision systems, automation frameworks, and human-in-the-loop workflows. Help clients identify where AI creates meaningful leverage versus unnecessary complexity.

• Quantify and anchor business value: Define and communicate ROI tied directly to revenue growth, cost savings, or operational efficiency. Ensure every solution is grounded in measurable outcomes and support outcome-based pricing and statement of work (SOW) development.

• Partner with leadership and delivery teams: Collaborate directly with the founding team and senior leadership on strategic opportunities. Work closely with engineering to ensure solutions are feasible, scalable, and aligned with delivery capabilities.

• Drive complex enterprise deals forward: Engage both technical and business stakeholders (CIO, CDO, Product, Operations), handle objections with clarity and logic, and guide conversations toward decisive outcomes rather than prolonged exploration.

Technical Expertise

• Strong understanding of data architecture, including data warehouses, pipelines, and streaming systems

• Experience with AI/ML systems, including LLMs, agent-based systems, and applied AI use cases

• Ability to design solution architectures that integrate with existing enterprise ecosystems

Builder Mindset

• Hands-on experience prototyping with SQL, Python, APIs, or dashboards

• Proven ability to quickly translate ideas into tangible proofs of concept

• Systems-level thinking, with a focus on end-to-end workflows rather than isolated features

Commercial Acumen

• Strong understanding of how businesses generate revenue and manage costs

• Ability to connect technical solutions directly to financial and operational outcomes

• Experience supporting or influencing complex deal cycles

Communication Skills

• Clear, structured, and concise communication style

• Ability to simplify complex technical concepts for diverse audiences

• Comfortable engaging with engineers, operators, and executive stakeholders

Experience

• 4+ years in solution engineering, data, AI, consulting, or product-focused roles

• Proven experience working in client-facing environments and managing ambiguity

• Track record of taking AI or data solutions from POC to production

• Experience supporting or closing enterprise AI/data platform deals ($500K–$5M+)

• Background in consulting or building data/AI products in production environments

• Experience with workflow automation, decision systems, or agent-based architectures

• Familiarity with modern data stacks and cloud platforms (GCP, AWS, Azure)