As a Lead Software Engineer at JPMorganChase within the AI/ML Technology, you will be a hands-on engineer responsible for building and shipping production software with a strong focus on AI-enabled capabilities. You will work across the full software development lifecycle—from requirements clarification and design through implementation, testing, deployment, and production support. You will develop backend services in Python, build APIs and micro-services, implement LLM-based solutions including agentic workflows, and deliver into a multi-cloud environment using Terraform, Kubernetes, and CI/CD pipelines. This role offers you the opportunity to grow your expertise in applied AI engineering while contributing to systems that operate at enterprise scale.
Job responsibilities
- Deliver software through a disciplined software development lifecycle, working from well-defined requirements through design, implementation, testing, release, and production support
- Write maintainable Python code with unit and integration tests, debugging issues across application, API, data-access, and runtime layers
- Implement LLM-driven workflows including prompting, tool and function calling, routing, orchestration, and state handling to support multi-step agentic task execution
- Build and maintain inference-time integrations such as model gateways, APIs, caching, fallbacks, timeouts, and concurrency controls for production AI systems
- Build REST and gRPC endpoints following agreed contracts, implementing authentication and authorisation integration, input validation, error handling, and secure data handling practices
- Implement SQL-backed business logic powering APIs and micro-services, including joins, aggregations, filtering, pagination, and transactional workflows
- Contribute to CI/CD pipelines and Git workflows, packaging and deploying services using containers and Kubernetes with support for safe rollouts and rollbacks
- Contribute to infrastructure-as-code using Terraform within established team patterns across modules, environments, and state management
Improve operability of services by adding and using observability tooling including logs, metrics, traces, dashboards, and alerts, and participate in incident response and root-cause analysis - Leverage enterprise-authorised AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards
- Apply knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorised AI-assisted development and automation capabilities, to improve the value realised by automation
- Formal training or certification on software engineering concepts and proficient applied experience
- Strong hands-on JavaScript, Java or Python development experience building backend services, including testing, packaging, dependency management, and maintainability
- Strong database and SQL proficiency, with experience implementing application logic and APIs on top of relational data
- Experience building APIs and micro-services using REST or gRPC, including contracts, security basics, and observability
- Practical experience delivering LLM-based features as part of software systems, with familiarity with agentic patterns
- Working knowledge of delivery and operations including CI/CD, and Git
- Familiarity with Kubernetes and cloud infrastructure concepts in a multi-cloud environment
- Solid understanding of software engineering fundamentals and software development lifecycle practices including design, reviews, testing, release, and production support
- Hands-on experience using enterprise-authorised AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs
- Strong experience with container orchestration platforms (Kubernetes distributions such as EKS, AKS, GKE or OpenShift, or managed equivalents such as ECS or Azure Container Apps), and with the cloud infrastructure that supports them.
- Experience with relational and/or NoSQL databases in the cloud (e.g. RDS/Aurora, DynamoDB, Azure SQL/Cosmos DB, GCP Cloud SQL/Firestore, Oracle).
Preferred skills
- Familiarity with Terraform and cloud infrastructure concepts in a multi-cloud environment
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations
- Strong debugging and troubleshooting skills in distributed systems, including root-cause analysis and performance bottleneck identification using logs, metrics, and traces
- Experience improving code quality and reliability through test strategy improvements, refactoring, static analysis, and dependency hygiene
- Experience with deployment and operational best practices including safe releases, rollbacks, environment configuration, and incident readiness
- Familiarity with common architecture patterns such as event-driven designs, async processing, caching, API versioning, and backward compatibility
- Experience collaborating effectively in agile delivery, including estimating, breaking down work, documenting decisions, and communicating risks