Applied AI Engineer - Agentic Systems - Senior Associate

JPMorgan Chase JPMorgan Chase · Banking · LONDON, LONDON, United Kingdom · Commercial & Investment Bank

Senior Associate Applied AI Engineer focused on building and deploying agentic AI systems, including multi-agent architectures, RAG pipelines, and tool integration, within a financial services context. The role involves productionizing autonomous agents, orchestrating complex workflows, and ensuring safety through prompt engineering and guardrails.

What you'd actually do

  1. Architect, develop, and productionize autonomous and assistive AI agents to enhance operations.
  2. Design multi-agent systems, including role definition, tool integration, planning, memory, and workflow orchestration using modern agent frameworks.
  3. Implement Retrieval-Augmented Generation (RAG) pipelines and semantic search with vector databases, including indexing, retrieval policies, and evaluation.
  4. Build and integrate agent tools and APIs to connect agents with external services, databases, and internal systems, ensuring robust output parsing and error handling.
  5. Practice advanced prompt engineering and implement output validation and guardrails to reduce hallucinations.

Skills

Required

  • Experience building and deploying agentic AI applications in production environments.
  • Expertise with ML frameworks such as PyTorch, TensorFlow, and scikit-learn.
  • Proficiency in Python; experience writing comprehensive tests and building evaluation harnesses for agents and prompts.
  • Hands-on experience with agent frameworks such as LangChain, CrewAI, AutoGen, LangGraph.
  • Knowledge of generative models including transformers, GANs, VAEs, and diffusion models.
  • Understanding of data preprocessing, feature engineering, and model/agent evaluation techniques.
  • Familiarity with cloud platforms and containerization technologies.
  • Strong problem-solving skills and ability to work independently and collaboratively.
  • Effective communication skills for technical and non-technical audiences.

Nice to have

  • Experience in financial services, especially investment banking operations.
  • Experience fine-tuning small language models with approaches like LoRA, QLoRA, DoRA; quantization and distillation.
  • Familiarity with prompt optimization frameworks and building prompt pipelines and evaluation suites.
  • Experience with distributed computing, data sharding, and performance optimization.
  • Hands-on experience with AWS services related to AI deployment and workflow orchestration.

What the JD emphasized

  • production environments
  • agentic AI applications
  • multi-agent systems
  • agent frameworks
  • guardrails
  • evaluation

Other signals

  • building multi-agent systems
  • productionize autonomous and assistive AI agents
  • design multi-agent systems
  • implement Retrieval-Augmented Generation (RAG) pipelines
  • build and integrate agent tools and APIs
  • practice advanced prompt engineering
  • implement output validation and guardrails
  • design microservices-based architectures
  • orchestrate multi-step workflows
  • instrument agents for tracing, metrics, and feedback loops
  • analyze data to inform agent capabilities
  • conduct A/B tests and performance evaluations
  • Experience building and deploying agentic AI applications in production environments
  • Hands-on experience with agent frameworks such as LangChain, CrewAI, AutoGen, LangGraph
  • Understanding of data preprocessing, feature engineering, and model/agent evaluation techniques