**Senior Lead Software Engineer - Java/Python, AI (Mumbai, India)**
Drive AI adoption across JPMorganChase by collaborating with internal business teams to identify and deliver high-value AI workflow solutions. As a Senior Lead, you'll translate workflows into secure, scalable prototypes, then guide them to production while meeting firm standards and promoting reusable patterns.
Leverage Java, Python, and AI technologies to design and build innovative solutions, with a strong focus on secure coding practices and responsible AI use. You'll partner with cross-functional teams, including product, engineering, and cybersecurity, to ensure successful implementations that improve code quality, delivery speed, and operational outcomes.
With 5+ years of experience in software engineering and a proven track record in AI-enabled workflows, you'll lead the adoption of enterprise-approved AI-assisted software development tools and practices. Demonstrate proficiency in AI concepts like large language models, prompt design, and secure software development to excel in this role.
Full Job Description
Location: Mumbai, Maharashtra, India
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 outcomesLead workflow discovery, map current-state processes, and define future-state AI-enabled designs with clear success metricsTranslate ambiguous problems into technical requirements, architecture options, acceptance criteria, and delivery plansBuild rapid prototypes and minimum viable solutions using firm-approved AI platforms, tools, and integration patternsDesign prompt patterns, retrieval-augmented generation workflows, and evaluation and feedback loops to improve solution qualityCoordinate with product, platform, cybersecurity, architecture, risk, legal, compliance, and controls partners to meet firm standardsDefine operational readiness requirements including monitoring, support models, escalation paths, and lifecycle management for deployed solutionsDrives 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 improvementsRequired qualifications, capabilities and skills
Formal training or certification in software engineering, computer science, data engineering, artificial intelligence, or a related discipline, or equivalent practical experience5+ years of applied experience in software engineering, solution engineering, platform engineering, data engineering, artificial intelligence engineering, or technical consultingHands-on experience with at least one modern programming language (for example Java, Python, or TypeScript) building production-quality solutionsExperience designing and delivering applications, integrations, application programming interfaces, services, workflow automation, or data-driven solutions in complex environmentsPractical knowledge of generative artificial intelligence concepts including large language models, prompt design, retrieval-augmented generation, embeddings, evaluation, and guardrailsStrong understanding of secure software development practices including authentication and authorization, secrets management, logging, monitoring, resiliency, and operational stabilityExperience working with cloud platforms, containerized applications, distributed systems, data platforms, or enterprise developer platformsAbility to translate business workflows into technical designs and communicate trade-offs to technical and non-technical stakeholdersExperience collaborating with product, engineering, cybersecurity, risk, compliance, and operations stakeholders to deliver governed solutionsDemonstrated 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 securityStrong 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 environmentHands-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 Build and scale secure AI workflow solutions with business teams, from discovery to prototype to production. 
