We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As an Applied AI/ML Senior associate within JPMorgan Chase Digital Technology, you will lead the innovation, development and deployment of various ML solutions that will sit at the intersection of scale, real business impact and engineering rigor.
Job Responsibilities
- Develop solutions related to data architecture, ML Platform as well as GenAI platform architecture, provide tactical solution and design support to the team and embedded with engineering on the execution and implementation of processes and procedures
- Serve as a subject matter expert on a wide range of ML techniques and optimizations.
- Provide in-depth knowledge of distributed ML platform deployment including training and serving.
- Create curative solutions using GenAI workflows through advanced proficiency in large language models (LLMs) and related techniques.
- Gain Experience with creating a Generative AI evaluation and feedback loop for GenAI/ML pipelines.
- Get Hands on code and design to bring the experimental results into production solutions by collaborating with engineering team.
- Own end to end code development in python/Java for both proof of concept/experimentation and production-ready solutions.
- Optimize system accuracy and performance by identifying and resolving inefficiencies and bottlenecks and collaborate with product and engineering teams to deliver tailored, science and technology-driven solutions.
- Drives decisions that influence the product design, application functionality, and technical operations and processes.
Required Qualifications, Capabilities, and Skills
- 4+ years of experience in building and deploying ML solutions (Deep learning/ LLMs).
- In-depth understanding of classical ML techniques, deep learning, graph networks, RAGs (and different flavours of RAG), and their applications to structured and unstructured datasets
- Good understanding of frameworks such as PyTorch or TensorFlow.
- In-depth understanding of Large Language Model (LLM) techniques, including Agents, Planning, Reasoning, and other related methods.
- Hands-on experience in building and deploying either one of these two - deep learning models or LLM based solutions
- Strong mathematical foundations on how the models work.
- Excellent coding skills, and the ability to quickly turn ideas into production-quality systems.
Preferred Qualifications, Capabilities, and Skills
- Bachelors or Masters in Computer Science and/or relevant fields.
- Experience in handling huge volumes of data (both batch and streaming), GPU optimization.
- Experience with search and recommender systems , and exposure to latest agentic frameworks such as Langchain, Langgraph, RASA, Parlant, Decagon.
- Application experience in prompt engineering, LLM model evaluation, and single and multi agent orchestration.
