We are hiring a Senior Solutions Engineer to help shape and scale our AI Data Solutions, working with leading AI labs, frontier model developers, and enterprise AI teams on complex data, evaluation, and model development workflows.
This is a senior, customer-facing role at the intersection of AI, solution design, commercial strategy, and operational execution. The right candidate will understand customer needs, translate ambiguous model development challenges into practical solutions, and help design scalable programs across human data, expert review, multilingual evaluation, speech/audio, transcription, model assessment, and post-training workflows.
The Senior Solutions Engineer will help define what we sell, how we scope it, and how we ensure it can be delivered at quality and scale.
Responsibilities
- Partner with Sales and senior leadership on strategic customer opportunities with AI labs, model development teams, and enterprise AI organizations.
- Lead discovery conversations with technical, research, product, operations, and executive stakeholders.
- Translate customer requirements into clear solution designs, workflows, pilot plans, proposals, and implementation approaches.
- Design solutions across model evaluation, multilingual data, speech/audio, transcription, expert review, post-training data, and quality workflows.
- Support RFPs, RFIs, proposals, pricing assumptions, and Statements of Work.
- Create customer-facing materials including solution narratives, workflow diagrams, pilot structures, and technical explanations.
- Work closely with Delivery, Quality, Supply Chain, and Technology teams to ensure proposed solutions are feasible, scalable, and commercially sound.
- Support clean handoff from Sales to Delivery, including requirements, assumptions, risks, and success criteria.
- Build reusable solution templates, scoping frameworks, and best practices for AI data and evaluation programs.
- Stay close to industry trends in model evaluation, benchmarking, RLHF, post-training data, agentic workflows, and multilingual AI.
What We’re Looking For
We are looking for a senior, commercially minded solutions professional who can engage credibly with sophisticated AI customers while also understanding the operational realities of delivering complex data programs.
You do not need to be a machine learning engineer, but you should be technically fluent enough to understand AI data workflows, ask the right questions, and design solutions that support real model development needs.
Qualifications
Education, skills, and experience
- 6+ years of experience in Solutions Engineering, Solutions Consulting, Technical Pre-Sales, AI Data Services, ML/Data Operations, Enterprise Technology, or a related role.
- Experience supporting complex B2B sales cycles with technical, operational, and executive stakeholders.
- Strong ability to translate ambiguous customer needs into clear requirements, solution designs, proposals, and delivery plans.
- Experience supporting discovery, RFP/RFI responses, pilots, proposals, and customer presentations.
- Strong written and verbal communication skills.
- Strong commercial judgment, with the ability to balance customer needs, feasibility, quality, timeline, cost, and margin.
- Comfort working in a fast-moving environment where solutions and processes are still being built.
Nice to have
- Experience with AI, machine learning, data services, model evaluation, annotation, transcription, localization, LLM evaluation, RLHF, or post-training workflows.
- Experience working with AI labs, enterprise AI teams, research organizations, or AI product companies.
- Familiarity with human-in-the-loop workflows, expert review, multilingual data, speech/audio, quality frameworks, or benchmark design.
- Experience designing pilots or proof-of-concept programs for strategic customers.
- Experience working with global delivery, contractor, crowd, or expert workforce models.
Success in This Role
- Customers view Argos as a credible partner for AI data and model evaluation.
- Sales teams are better equipped to engage sophisticated AI buyers.
- Pilots are clearly scoped, commercially sound, and operationally executable.
- Delivery teams receive clean handoffs with clear requirements and success criteria.
- Repeatable solution frameworks are created for common AI data and evaluation use cases.
- Argos is better positioned with frontier AI labs and high-value enterprise AI customers.
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