Make your mark by building agentic artificial intelligence solutions that turn complex business needs into measurable outcomes. Join a collaborative engineering team where you will design, deploy, and improve production-grade machine learning systems using modern large language model techniques. Grow your career through high-impact work, strong mentorship, and opportunities to expand your technical depth and product delivery skills.

As an Applied AI ML Associate in JPMorgan Chase, you design and deliver secure, stable, and scalable technology products using machine learning. You build and productionize agent-based solutions and data-driven systems that solve real business problems. You partner with engineers, data professionals, and stakeholders to define requirements, evaluate approaches, and deliver measurable improvements. You apply strong software engineering practices to testing, release, and support.

Job Responsibilities

Design and deliver agent-based artificial intelligence solutions that address defined business use cases from prototype through production deployment.
Implement human-in-the-loop workflows, fallback behaviors, and tool-calling patterns to improve reliability, traceability, and user outcomes.
Evaluate candidate language models for specific use cases by defining metrics, running experiments, and documenting performance, limitations, and trade-offs.
Build reusable artificial intelligence tools and integrations that connect models and agents to enterprise data and services.
Develop end-to-end machine learning pipelines that prepare data, train or adapt models, validate quality, and support repeatable deployments.
Apply retrieval-augmented generation methods to improve answer accuracy and grounding on large datasets.
Use deep learning techniques (including attention-based architectures) to support solutions such as semantic search, entity resolution, forecasting, and anomaly detection where appropriate.
Own small-to-medium engineering deliverables end-to-end, including requirements clarification, design, implementation, testing, release, and post-release support.
Collaborate with cross-functional partners to establish baselines, analyze results, and translate findings into actionable improvements.
Incorporate governance, risk, and control considerations into solution design and delivery to support safe and responsible use of machine learning capabilities.

Required qualifications, capabilities, and skills

Master's degree or higher in Computer Science, Data Science, or a related discipline (or equivalent).
3+ years of industry experience, including a minimum of 2 years hands-on experience as a machine learning engineer, data engineer, or data scientist.
Demonstrated experience applying machine learning methods such as regression, classification, clustering, time series modeling, causal inference, or mathematical optimization.
Hands-on experience designing and building agent-based solutions and tool integrations using agent frameworks (for example, Google ADK, LangGraph) and agent-to-agent communication patterns.
Proficiency in Python for production-oriented development (testing, packaging, and deployment practices).
Hands-on experience building data pipelines and working with large datasets experience with PySpark is acceptable where relevant.
Working knowledge of deep learning frameworks (TensorFlow or PyTorch) and modern natural language processing techniques.
Strong written and verbal communication skills with experience partnering with stakeholders across engineering and business functions.

Preferred qualifications, capabilities, and skills

Experience building multi-agent solutions in enterprise environments.
Experience leading data science or machine learning workstreams end-to-end (problem framing, experimentation, deployment, measurement).
Experience designing large-scale machine learning systems (latency, cost, monitoring, and reliability considerations).
Experience with cloud-based machine learning services and production pipelines.
Experience with the Amazon Web Services machine learning ecosystem.
Experience optimizing model training or inference using graphics processing units where required.
Familiarity with transformer-based embedding models (for example, BERT, Sentence-BERT) and prompt optimization techniques.