Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.
As a Senior Lead Software Engineer at JPMorgan Chase within the Enterprise Technology - Public Cloud Engineering team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.
As a Senior Machine Learning and Generative AI Engineer in Public Cloud Engineering, you will lead hands-on architecture, development, and production deployment of ML and LLM-powered solutions. You’ll apply strong engineering practices, rigorous experimentation, and responsible AI methods to deliver high-impact capabilities for our businesses, partnering across a global, multidisciplinary team.
Job responsibilities
Design and implement end-to-end ML and LLM solutions, from problem framing and data preparation through training, evaluation, deployment, and ongoing optimization.Apply modern GenAI workflows, including prompt engineering techniques, tracing, evaluations, guardrails, and safety frameworks to align model behavior with business objectives and risk controls.Productionize high-quality models and pipelines on public clouds, leveraging Kubernetes for container orchestration where appropriate.Establish robust offline and online evaluation methodologies, including intrinsic and extrinsic metrics (e.g., relevance, safety, latency, cost efficiency), and integrate automated testing/monitoring.Collaborate closely with product, platform, security, controls, and business stakeholders across a geographically distributed organization; provide technical mentorship and code reviews.Document solution designs and decisions; contribute to reusable components, patterns, and best practices for ML/GenAI in public cloud environments.Optimize for cost, performance, and resilience; incorporate data privacy, compliance, and responsible AI considerations throughout the lifecycle.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experienceMS or PhD in Computer Science, Data Science, Statistics, Mathematical Sciences, or Machine Learning; strong background in mathematics and statistics.Extensive expertise applying data science and ML to business problems with strong programming in Python and/or Java.Hands-on experience with GenAI/LLMs (e.g., GPT, Claude, Llama or similar), including prompt engineering, tracing, evaluations, and guardrails.Solid background in NLP and Generative AI; strong understanding of ML and deep learning methods and large language models.Extensive experience with ML/DL toolkits and libraries (e.g., Transformers, Hugging Face, TensorFlow, PyTorch, NumPy, scikit-learn, pandas).Demonstrated leadership in proposing and delivering AI/ML and GenAI solutions; ability to drive technical direction and influence stakeholders.Experience designing experiments, training frameworks, and metrics aligned to business goals.Expertise with at least one major public cloud (AWS, GCP, or Azure) and with containerization/orchestration (Docker/Kubernetes).Strong grounding in data structures, algorithms, ML, data mining, information retrieval, and statistics.Excellent communication skills, with the ability to engage senior technical and business partners. Preferred qualifications, capabilities, and skillsDepth in one or more: Natural Language Processing, Reinforcement Learning, Ranking/Recommendation, or Time Series Analysis.Additional familiarity with ML frameworks (e.g., PyTorch, Keras, MXNet, scikit-learn).Understanding of financial services or wealth management domains.Desirable: Contributions to open-source ML/LLM tooling; certifications in AWS, Azure, GCP, or Kubernetes.