Sales Engineer
Posted 3hrs ago
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Job Description
Sales Engineer designing AI/ML architectures and scoping technical engagements for Factored, which helps U.S. companies build data and AI teams with LATAM talent.
Responsibilities:
- Lead technical discovery calls with prospects, surfacing requirements, constraints, and success criteria across data engineering, ML, data science, analytics, and AI engineering use cases.
- Design and present ML, AI, and Data Science system architectures live with clients, including RAG, agentic workflows, LLM evaluation pipelines, model serving infrastructure, and end-to-end MLOps.
- Translate ambiguous business problems into scoped, deliverable engagements by defining solution approaches, identifying risks, sizing effort, and shaping statements of work alongside Account Executives.
- Serve as the deal-side technical authority, defending architectural choices and challenging suboptimal designs.
- Build technical proposals, reference architectures, and proof-of-concept plans.
- Partner with delivery leadership to hand off won deals, ensuring scope, assumptions, and technical context transfer accurately.
- Develop and maintain reusable solution patterns, demos, and reference implementations.
- Stay current on the AI/ML landscape and apply that knowledge in client conversations.
- Provide feedback to product, delivery, and marketing on prospect needs and offering improvements.
- Represent Factored externally through webinars, technical content, conference conversations, and select customer events.
Requirements:
- 5+ years of total professional engineering experience, with a clear track record of increasing technical responsibility and scope.
- 3+ years working hands-on as a Senior Engineer in AI Engineering, Machine Learning Engineering, Data Engineering or an adjacent production-focused role.
- Demonstrable expertise in machine learning system design: ability to whiteboard end-to-end ML/AI systems live, reason about trade-offs (latency, cost, accuracy, maintainability), and defend choices under technical scrutiny.
- Deep practical experience with GenAI/LLM systems: RAG architectures, agentic patterns, evaluation frameworks, prompt and context engineering, fine-tuning approaches, and inference infrastructure.
- Working fluency across the broader data stack: data engineering pipelines (batch and streaming), warehouses/lakehouses, feature stores, and analytics workflows — enough to credibly scope work outside pure AI/ML.
- Advanced English proficiency, with exceptional written and verbal communication — capable of holding the room with both senior technical stakeholders and executive buyers.
- Strong client-facing instincts: comfortable presenting, listening for the real problem under the stated one, and converting ambiguity into a concrete plan.
- Track record of execution in fast-paced, ambiguous environments — owns outcomes end-to-end without needing tight supervision.
- Structured thinking under time pressure: can decompose a half-formed client problem into a clean architecture in a 60-minute call.
Benefits:
- Ownership through equity participation.
- Annual company retreat.
- Education bonus for continuous learning.
- Company-wide winter break.
- Paid time off.
- Optional in-person events and meetups.
- Tailored career roadmaps.
- High-performance culture.


















