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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Remote - **Company:** MAG 24 LLC - **Location:** New York, NY, United States (Remote available) - **Salary:** $300,000.0 - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Big Data, Data Intelligence, Machine Learning, AI Infrastructure, Large Language Models, Multi-Agent Systems, Machine Learning Operations, Data Pipelines - **Published:** September 17, 2026 - **Apply:** https://www.careerjet.com/jobad/us31c88f4714e813d46c3109a91916299f ## About the Role * Strong professional experience as a software, machine-learning, applied AI, or infrastructure engineer * Advanced Python engineering skills with experience building and shipping production systems end to end * Practical experience with LLMs, agentic systems, multi-turn workflows, tool use, RAG, or AI automation * Experience building or maintaining data pipelines, ML infrastructure, model-evaluation systems, or research workflows * Strong understanding of data quality, taxonomy design, labelling workflows, and dataset curation for AI systems * Ability to operate independently in ambiguous, partner-facing environments with strong technical and product ownership * Comfortable working directly with researchers, technical partners, founders, and enterprise stakeholders * Experience within a startup, AI infrastructure company, applied AI organisation, or research-focused engineering team is advantageous * Experience building multi-turn agents, agent-evaluation systems, workflow automation, or human-in-the-loop AI is advantageous * Familiarity with modern LLM tooling, agent frameworks, model-evaluation stacks, and ML experimentation platforms is beneficial * Experience acting as a technical partner to external customers, research teams, or strategic enterprise accounts is strongly valued ## Description We are sharing a specialised full-time opportunity for experienced Forward Deployed Engineers to work at the intersection of applied AI, machine-learning infrastructure, data intelligence, and partner-facing technical implementation. Selected professionals will work directly with leading AI research and enterprise teams to translate complex or ambiguous AI problems into scoped technical projects and production systems. The role combines research collaboration, ML and evaluation infrastructure, large-scale data systems, LLM applications, and hands-on engineering across the full lifecycle from discovery and architecture through deployment, iteration, and partner success., Applied AI & Partner-Facing Engineering * Work directly with AI research teams and enterprise partners to define research goals, technical requirements, and project direction * Translate ambiguous AI and machine-learning problems into clearly scoped technical projects * Act as a technical implementation partner across research, engineering, product, and stakeholder teams * Move comfortably between research questions, technical architecture, hands-on engineering, and partner-facing execution * Own technical systems from initial discovery through implementation, deployment, iteration, and ongoing reliability ML Infrastructure & Data Intelligence * Build large-scale data-intelligence systems for collecting, organising, evaluating, and improving training and evaluation data * Implement ML pipelines supporting data curation, model training, evaluation, experimentation, and continuous improvement * Develop infrastructure for model inference, experimentation, evaluation, and deployment * Design reliable technical workflows that support advanced AI research and production environments * Build and maintain systems capable of supporting complex data and machine-learning workloads LLM Applications & Agentic Systems * Develop LLM applications including multi-agent systems, tool-using agents, RAG workflows, and human-in-the-loop systems * Build evaluation harnesses and infrastructure for assessing agent and model behaviour * Develop systems that extend one-off AI experiments into reliable, repeatable, multi-turn workflows * Implement agentic automation across technically complex business and research processes * Apply modern LLM tooling and model-evaluation approaches to production-oriented AI systems Data Quality, Taxonomy & Evaluation * Design data taxonomies, labelling systems, evaluation rubrics, and quality frameworks * Improve dataset structure and quality to support stronger model performance and research outcomes * Develop workflows for dataset curation, annotation, validation, and quality assurance * Analyse data and model behaviour to identify opportunities for system improvement * Apply rigorous standards to training data, evaluation datasets, and research workflows ## Related Videos - [Why and when should we consider Stream Processing frameworks in our solutions](https://www.wearedevelopers.com/videos/1085-why-and-when-should-we-consider-stream-processing-frameworks-in-our-solutions) - [Agentic employees in world's most downloaded FinTech app](https://www.wearedevelopers.com/videos/100123-agentic-employees-in-world-s-most-downloaded-fintech-app) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Fireside Chat: Deep Learning, Deep Impact: Harnessing AI for Language Innovation](https://www.wearedevelopers.com/videos/612-fireside-chat-deep-learning-deep-impact-harnessing-ai-for-language-innovation) - [Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation](https://www.wearedevelopers.com/videos/1235-carl-lapierre-exploring-advanced-patterns-in-retrieval-augmented-generation) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)