Join a high-impact team helping internal teams turn AI into measurable outcomes through hands-on engineering and practical delivery. You will shape reusable patterns that accelerate adoption across the firm.
As a Forward Deployed Engineer – AI Enablement at JPMorganChase within the AI Enablement and Data Platform Technology Engineering team, you will partner directly with internal business teams to identify high-value workflow opportunities and deliver AI-enabled solutions. You will translate real workflows into secure, scalable prototypes and guide the path to production with appropriate controls. You will collaborate closely with product, engineering, cybersecurity, risk, compliance, and operations partners to meet governance and operational readiness expectations. You will help convert successful implementations into reusable capabilities that can scale across multiple teams.
Job responsibilities
- Partner with internal business teams to discover, assess, and prioritize AI-enabled workflow opportunities tied to measurable outcomes
- Lead workflow discovery, map current-state processes, and define future-state AI-enabled designs with clear success metrics
- Translate ambiguous problems into technical requirements, architecture options, acceptance criteria, and delivery plans
- Build rapid prototypes and minimum viable solutions using firm-approved AI platforms, tools, and integration patterns
- Design prompt patterns, retrieval-augmented generation workflows, and evaluation and feedback loops to improve solution quality
- Coordinate with product, platform, cybersecurity, architecture, risk, legal, compliance, and controls partners to meet firm standards
- Define operational readiness requirements including monitoring, support models, escalation paths, and lifecycle management for deployed solutions
- Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
- Measure adoption and impact using agreed key performance indicators such as time saved, cycle-time reduction, and quality improvements
Required qualifications, capabilities and skills
- Formal training or certification in software engineering, computer science, data engineering, artificial intelligence, or a related discipline, or equivalent practical experience
- 5+ years of applied experience in software engineering, solution engineering, platform engineering, data engineering, artificial intelligence engineering, or technical consulting
- Hands-on experience with at least one modern programming language (for example Java, Python, or TypeScript) building production-quality solutions
- Experience designing and delivering applications, integrations, application programming interfaces, services, workflow automation, or data-driven solutions in complex environments
- Practical knowledge of generative artificial intelligence concepts including large language models, prompt design, retrieval-augmented generation, embeddings, evaluation, and guardrails
- Strong understanding of secure software development practices including authentication and authorization, secrets management, logging, monitoring, resiliency, and operational stability
- Experience working with cloud platforms, containerized applications, distributed systems, data platforms, or enterprise developer platforms
- Ability to translate business workflows into technical designs and communicate trade-offs to technical and non-technical stakeholders
- Experience collaborating with product, engineering, cybersecurity, risk, compliance, and operations stakeholders to deliver governed solutions
- Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls.
Preferred qualifications, capabilities and skills
- Experience enabling artificial intelligence, automation, analytics, or workflow transformation initiatives in a large enterprise or regulated environment
- Hands-on experience with enterprise artificial intelligence platforms (for example assistants, document analysis, knowledge retrieval, or agentic workflow tooling)
- Experience designing and delivering retrieval-augmented generation solutions including vector search, document ingestion, grounding strategies, and answer evaluation