Software Engineer, AI (Senior/Staff)
M
Monarch MoneyFinTech
Remote (US), 9 AM – 2 PM PTFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonMachine LearningPrompt Engineering
Requirements
- 5+ years of experience in software engineering
- At least 2 years focused on building and operating production ML/AI systems
- Proven track record of shipping LLM-powered features
- Deep, hands-on expertise in prompt engineering
- Deep, hands-on expertise in RAG systems
- Deep, hands-on expertise in evaluation techniques
- Strong fundamentals in machine learning: embeddings, similarity search, classification, and probabilistic reasoning
- Demonstrated experience building and using AI evaluation tooling (e.g., golden sets, rubric scoring, LLM-as-judge)
- Excellent Python skills
- History of building production-grade AI features and services
- Strong collaboration and communication skills
- Sharp product sensibility
- Strategic mindset, comfortable making build-vs-buy decisions
- Designing features for long-term reliability
Responsibilities
- Design, build, and own AI features to help users understand and manage their money.
- Work across the full spectrum of AI development, from prompt engineering and API integrations to building multi-agent systems and fine-tuning language models.
- Make critical decisions on conversational AI architecture and how to evaluate and ship AI features.
- Collaborate closely with the AI Platform team, focusing on the AI application layer.
- Apply GenAI and ML to help users make sense of their money, understanding spending patterns, surfacing actionable insights, or automating financial tasks.
- Choose the right AI toolkit thoughtfully, balancing innovation with pragmatism to ship features that work reliably at scale.
- Leverage and enhance the sophisticated evaluation framework to ensure AI quality, designing test datasets, implementing new scorers, and validating changes before release.
- Own AI feature development, agent design and orchestration, ML model improvements, evaluation datasets and scorers, prompt engineering, and feature-level quality.
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