Applied AI ML Senior Associate

Are you ready to be an integral part of an agile team that consistently pushes the boundaries to enhance, build, and deliver top-tier technology productsAt JPMorgan Chase, within the Corporate Sector - AIML Data Platforms, we are seeking a Applied AIML Software Engineer who will play a crucial role in driving business impact through technical expertise and innovative problem-solving.

As a Software Engineer within the AI/ML Platform team you will be building a GenAI apps and Multi Agent AI framework that helps teams diagnose and resolve production issues, manage infrastructure and perform complex SRE and DevOps workflows.

Job Responsibilities

• Serves as a subject matter expert on a wide range of ML techniques and optimizations.
• Provides in-depth knowledge of ML algorithms, frameworks, and techniques.
• Design and evolve systems that use intelligent agents that assist users in detecting, triaging, and resolving incidents using observability data and tools.
• Build end-to-end workflows for incident lifecycle management, powered by LLMs or agentic technologies.
• Implement agentic systems to automate workflows, suggest improvements, and surface likely root causes of failures.
• Develop Model Context Protocol (MCP) server for observability tooling
• Build APIs and orchestration logic for chaining agent actions into multi-step workflows.
• Integrate agentic applications with internal platforms and third-party tools.
• Conducts experiments using latest ML technologies, analyzing results, tuning models

Required Qualifications, Capabilities, And Skills

• MS or BS in Computer Science, Machine Learning, or a related field, with at least 5 years of experience as a software engineer.
• At least 2 years experience in applying data science, ML techniques to solve business problems.
• At least 2 years experience in one of the programming languages like Python, Java, C/C++, etc.
• Solid background in Natural Language Processing (NLP) and Large Language Models (LLMs)
• Experience with model training, fine-tuning, and evaluation methodologies
• Experience using Agentic AI frameworks such as LangGraph, LangChain, and other orchestration tools to build scalable agent-based systems.
• Ability to work on tasks and projects through to completion with limited supervision.
• Hands-on experience with AI/ML frameworks (TensorFlow, PyTorch, JAX, scikit-learn).

Preferred qualifications, capabilities, and skills

• Experience with observability tools such as Datadog, Splunk, CloudWatch, Grafana, ELK, and OpenTelemetry.
• Familiarity with AIOps platforms and techniques including anomaly detection, event correlation, and predictive analytics