Job description

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) We have an opportunity to impact your career and provide an adventure where you can push the limits of what’s possible. As a Lead AI Engineer at JPMorganChase, you will be an integral part of an agile team that enhances, builds, and delivers trusted, market-leading technology products in a secure, stable, and scalable way. You will work across a portfolio of products to embed AI capabilities - including automation, AI-powered applications, and agentic workflows - while upholding strong engineering standards, architecture discipline, and risk controls. This role requires a strong AI-forward mindset. We are looking for engineers who don’t just use AI - they think with it, build with it, and know when not to use it.

Requirements

Must-haves

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)

Nice-to-haves

Experience with cloud platforms (AWS preferred) and containerized deployments
Preferred Qualifications, Capabilities, and Skills

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmpythonreactawsci/cdagile

Platform: oracle-cloud
Effort: medium