Applied AI Engineer [32879]

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.

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 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