Senior Solutions Engineer, AI Data – Model Evaluation Solutions

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🕒 4 days ago

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Argos Multilingual

WebsiteLinkedInAll Job Openings

201 - 500 employees

💼 Consulting

🏥 Healthcare

🏭 Manufacturing

Consulting • Healthcare • Manufacturing

Argos Multilingual is a global language-services provider that delivers translation, localization, and multilingual content solutions combining human expertise with AI-first technology. The company offers translation and localization, multimedia services, language quality evaluation and ownership programs, content and linguistic asset management, and AI/LLM data solutions (data collection, annotation, evaluation, RLHF, and custom machine translation) through its MosAIQ suite of enterprise tools. Argos serves B2B customers across regulated and technical sectors—life sciences, medical/healthcare, technology, finance, manufacturing, and retail—focusing on accuracy, compliance, and scalable AI-enabled workflows.

📋 Description

• 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 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 solution narratives, workflow diagrams, pilot structures, and technical explanations • Collaborate with Delivery, Quality, Supply Chain, and Technology teams to ensure solutions are feasible, scalable, and commercially sound • Support Sales-to-Delivery handoffs, including requirements, assumptions, risks, and success criteria • Build reusable solution templates, scoping frameworks, and best practices for AI data and evaluation programs • Track industry trends in model evaluation, benchmarking, RLHF, post-training data, agentic workflows, and multilingual AI

🎯 Requirements

• 6+ years of experience in Solutions Engineering, Solutions Consulting, Technical Pre-Sales, AI Data Services, ML/Data Operations, Enterprise Technology, or a related role • Working knowledge of Python and SQL • Ability to understand data pipelines, workflow logic, data validation, and customer technical requirements • Hands-on experience with APIs, JSON, CSV, XML, data schemas, file transfer methods, and common integration patterns • Comfort discussing cloud-based workflows, data storage, annotation/evaluation platforms, and operational tooling with technical teams • Familiarity with AI/ML concepts, LLM evaluation, benchmarking, RLHF/post-training workflows, or model development pipelines • Experience supporting complex B2B sales cycles with technical, operational, and executive stakeholders • Ability to translate ambiguous customer needs into 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 balancing customer needs, feasibility, quality, timeline, cost, and margin • Comfort working in a fast-moving environment where solutions and processes are still being built • Experience with JavaScript, Java, C#, PHP, or other programming languages is a plus • Experience with AI, machine learning, data services, model evaluation, annotation, transcription, localization, LLM evaluation, RLHF, or post-training workflows is nice to have • Experience working with AI labs, enterprise AI teams, research organizations, or AI product companies is nice to have • Familiarity with human-in-the-loop workflows, expert review, multilingual data, speech/audio, quality frameworks, or benchmark design is nice to have • Experience designing pilots or proof-of-concept programs for strategic customers is nice to have • Experience working with global delivery, contractor, crowd, or expert workforce models is nice to have