We are looking for an Applied AI Engineer to help design, train, and deploy production-grade AI models powering next-generation AI agents in financial workflows. This role focuses on fine-tuning, post-training optimization, and building reliable model pipelines using both open-source and proprietary data.
Please read the information in this job post thoroughly to understand exactly what is expected of potential candidates.
You will work closely with product, engineering, and domain experts to translate business problems into scalable AI systems.
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Key Responsibilities
Model Development & Training
• Fine-tune large language models and multimodal models for domain-specific use cases
• Design post-training pipelines (instruction tuning, RLHF, evaluation loops, etc.)
• Implement and optimize training workflows using open-source frameworks
• Experiment with model architecture improvements and hyperparameter optimization
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AI Agent & System Integration
• Build and improve AI agents capable of executing multi-step workflows
• Integrate models into production environments and product features
• Improve model reliability, accuracy, and robustness for high-stakes applications
• Develop evaluation and testing frameworks for model performance and edge cases
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Data & Experimentation
• Work with proprietary domain datasets to improve model specialization
• Design training datasets, labeling pipelines, and data quality frameworks
• Conduct ablation studies and performance benchmarking
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Cross-Functional Collaboration
• Partner with product teams to design AI-first product experiences
• Provide technical feasibility insights for roadmap and feature planning
• Collaborate with global engineering teams on implementation and scaling
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Required Qualifications
• 1–4 years of hands-on experience training or fine-tuning ML/AI models
• Experience working with LLM or multimodal model training pipelines
• Strong Python skills and familiarity with deep learning frameworks such as:
• PyTorch
• Hugging Face ecosystem
• Open-source training frameworks
• Experience deploying ML models into production systems
• Understanding of evaluation methodologies and model performance tradeoffs
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Preferred Qualifications
• Experience xsgimln with post-training techniques such as:
• Instruction tuning
• RLHF / preference optimization
• Model distillation
• Experience building AI agent or workflow automation systems
• Familiarity with distributed training and GPU optimization
• Experience working with domain-specific data (finance, compliance, enterprise workflows, etc.)
• Background working in early-stage startups or research labs
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Nice to Have (Bonus Skills)
• Exposure to pre-training or large-scale model training environments
• Experience with retrieval-augmented generation (RAG)
• Experience designing evaluation benchmarks for enterprise AI applications
• Experience working with multi-agent systems or tool-use models