Software Engineer III - AI/ML, 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 databases—including vector stores (e.g., Pinecone, pgvector), graph databases, and relational/NoSQL systems—for 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