Job Description
We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
As a Software Engineer III at JPMorgan Chase within the Asset & Wealth Management, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Design, develop, and deploy LLM models and pipelines to tackle business challenges and opportunities
- Utilize techniques like prompt engineering, model fine-tuning, RAGs, and LLM Agents for use cases such as Q&A, summarization, and automation
- Employ firm-standard tools and platforms to set up automated AI/ML pipelines that can be quickly re-trained and deployed
- Collaborate with stakeholders and cross-functional teams to gather requirements and comprehend ML/LLM use cases and data needs
- Optimize and maintain cloud-based infrastructure to facilitate rapid ML model deployment and integration with workflows
- Conduct data analysis and preprocessing to prepare datasets for model training and evaluation
- Monitor and maintain deployed models, implementing necessary updates and improvements to address business priorities and model drift
- Stay informed about the latest advancements in machine learning, GenAI, and NLP technologies
- Communicate findings and insights effectively to both technical and non-technical stakeholders.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 3+ years applied experience
- Experience in ML/LLM model development, deployment, and integration.
- Strong programming skills in Python and experience with ML frameworks such as TensorFlow, PyTorch, and scikit-learn.
- Good understanding of statistical concepts of hypothesis testing, data distributions and inference underlying and governing choice and working of ML algorithms.
- Strong fundamentals in natural language processing (NLP) and experience using large language models (LLMs) such as GPT, Llama, BERT, or similar.
- Familiarity with containerization technologies like Docker and orchestration tools like Kubernetes.
- Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) and services like Sagemaker/Azure ML for deploying machine learning models.
- Strong problem-solving skills and the ability to work independently and collaboratively in a fast-moving organizational setup.
- Excellent problem-solving skills and the ability to work independently and collaboratively.
- Strong communication and interpersonal skills, with experience in working with stakeholders and cross-functional teams.
Preferred qualifications, capabilities, and skills
- Experience with big data technologies such as Spark is a big plus.
- Experience of CI/CD pipelines and DevOps practices/MLOps for ML model deployment.
- Demonstrable portfolio of projects and Open source community contributions.
