Sr Lead Software Engineer - Cloud / ML / GenAI

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 experience
• MS 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.

• Depth 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.