Lead the design, development, and deployment of production-grade AI applications, including LLM workflows, RAG systems, and agentic solutions. Establish AI engineering standards, architecture practices, observability, governance, security, and risk controls. Build automation and secure Python services, evaluate vendors and emerging technologies, and partner across product, engineering, legal, and risk teams. Mentor teams through documentation, workshops, and technical reviews while delivering reliable, scalable, cost-effective AI capabilities.
Job Responsibilities
- Enhance multiple products across the product line by identifying, prioritizing, and delivering AI-driven features that improve user experience, efficiency, and operational resilience
- Design, build, and deploy production-grade AI applications including LLM-powered workflows, Retrieval-Augmented Generation (RAG) solutions, and agentic systems
- Build agentic workflows with enterprise-ready controls (identity, authorization, guardrails, tracing, evaluation, and auditability)
- Develop automation solutions that reduce manual effort and eliminate recurring operational issues, including self-healing patterns and intelligent triage
- Architect AI systems with production concerns in mind: latency, scalability, cost management, model selection, observability, reliability, and failure modes (including hallucinations and tool errors)
- Establish and apply AI engineering standards across the product line (prompting patterns, RAG design standards, evaluation practices, logging/telemetry, and release readiness criteria)
- Drive AI architecture reviews and contribute to reference architectures for model hosting, orchestration, data access, and secure tool execution
- Identify and mitigate risks across the AI lifecycle including data privacy, security, bias, model misuse, prompt injection, and third-party/vendor risk, and ensure appropriate documentation and controls
- Partner with product, design, risk, legal/compliance, and engineering stakeholders to align on outcomes, constraints, and measurable success metrics
- Lead technical evaluations with vendors and platform providers; assess capabilities, integration fit, security posture, and total cost of ownership
- Research emerging AI capabilities (models, frameworks, tools) and translate findings into practical guidance, prototypes, and adoption recommendations
- Educate and enable teams through documentation, workshops, design reviews, and coaching to raise AI fluency and delivery consistency across the organization
- Execute creative software solutions, technical troubleshooting, and hands-on development; write secure, high-quality production code and review/debug code written by others
- Influence stakeholders and drive alignment across teams without direct authority, owning outcomes end-to-end
Required Qualifications, Capabilities, and Skills
- Formal training or certification in software engineering concepts and 5+ years of applied engineering experience
- Hands-on experience delivering AI-powered features in production or near-production environments (LLMs, RAG, agents, workflow automation, or decision support systems)
- Strong proficiency in Python (and ability to build maintainable services, libraries, and pipelines)
- Experience building APIs and services using Django, FastAPI, or similar frameworks
- Experience building modern UIs using React or similar frameworks (or strong ability to partner closely with frontend teams)
- Strong system design skills with an emphasis on secure distributed systems, reliability, and operational excellence
- Practical understanding of Responsible AI / model risk concepts (evaluation, guardrails, privacy, bias, explainability where applicable, and safe deployment practices)
- Experience implementing observability (logging, metrics, tracing) and AI-specific monitoring (quality, drift signals, latency, cost, error rates)
- Experience with cloud platforms (AWS preferred) and containerized deployments
- Strong problem-solving skills with a structured, evidence-based approach; comfortable operating in ambiguity and delivering iteratively
Preferred Qualifications, Capabilities, and Skills
- Experience with AI orchestration and agent frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel, CrewAI, or similar)
- Experience with vector search and retrieval patterns including pgvector, Pinecone, Weaviate, OpenSearch/Elastic, or similar
- Experience designing and operating RAG systems (chunking strategies, retrieval evaluation, citation/attribution patterns, caching, fallback behaviors)
- Familiarity with AI governance and standards (model documentation, risk assessments, control frameworks, secure-by-design patterns) and applying them to real delivery timelines
- Strong Infrastructure-as-Code experience (e.g., Terraform) and CI/CD practice, data engineering ecosystems (e.g., Databricks, streaming, data quality controls, lineage concepts)
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
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Home to tech titans like Microsoft and Amazon, Seattle punches far above its weight in innovation. But its surrounding mountains, sprinkled with world-famous hiking trails and climbing routes, make the city a destination for outdoorsy types as well. Established as a logging town before shifting to shipbuilding and logistics, the Emerald City is now known for its contributions to aerospace, software, biotech and cloud computing. And its status as a thriving tech ecosystem is attracting out-of-town companies looking to establish new tech and engineering hubs.
Key Facts About Seattle Tech
- Number of Tech Workers: 287,000; 13% of overall workforce (2024 CompTIA survey)
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- Funding Landscape: $3.1 billion in venture capital funding in 2024 (Pitchbook)
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- Research Centers and Universities: University of Washington, Seattle University, Seattle Pacific University, Allen Institute for Brain Science, Bill & Melinda Gates Foundation, Seattle Children’s Research Institute
