Software Engineer III AIML, Prompt Engineer

You are a strategic thinker passionate about building prompt-driven production-grade AI that measurably improves risk and control outcomes across enterprise workflows. You have found the right team.

As a Prompt Engineering & Applied ML Associate at JPMorgan Chase as a part of our Risk & Controls AI team you will spend each day translating business intent into grounded auditable decision support by combining prompt engineering with applied machine learning

Job responsibilities

• Design production-grade prompts for complex enterprise workflows; test iterate and optimize based on outcomes.
• Apply familiarity with ML models including classification NLP and transformer-based architectures. Implement LLM integration patterns such as retrieval-augmented generation (RAG) chain-of-thought prompting and response validation.
• Define and execute prompt/model evaluation criteria (accuracy consistency hallucination rate policy adherence).
• Design and run offline and online experiments to improve prompts and tune model performance.
• Implement guardrails safety filters and fallback strategies in production AI/ML workflows. Use ML frameworks such as PyTorch TensorFlow scikit-learn and/or Hugging Face Transformers.
• Build multi-step agent workflows using LangChain or similar orchestration frameworks.
• Leverage modern databasesincluding vector stores (e.g. Pinecone pgvector) graph databases and relational/NoSQL systemsfor retrieval and persistence.
• Optimize embeddings usage tokenization strategies and context-window management. Integrate model serving infrastructure API-based model providers and model routing strategies.
• Prepare data engineer features and curate datasets for ML training and evaluation. Monitor AI/ML-assisted production workflows with observability and drift detection practices. Translate complex business requirements into structured prompt model and system designs.
• Develop Python-based orchestration data transformation and automation scripts. Collaborate effectively with software engineers product owners and control stakeholders.
• Communicate clearly in writing and verbally to both technical and business audiences.

Required qualifications capabilities and skills

• BS/BA degree in Computer Science Engineering Machine Learning Data Science Statistics or equivalent experience
• 5 years of hands-on experience in machine learning applied AI or prompt engineering in production environments
• 5 years of experience with Python and ML/AI tooling (model development evaluation and deployment)
• 3 years of experience with LLM application development prompt engineering or NLP systems
• 1 years of experience with experimentation frameworks A/B testing and production monitoring of AI features

Preferred qualifications capabilities and skills

• Experience with responsible AI practices and model governance
• Experience in financial services technology risk or controls-oriented environments
• Relevant ML/AI certifications (e.g. AWS ML Specialty Google Professional ML Engineer)
• Understanding of enterprise risk controls and auditability expectations