Jobs at AgileEngine

Own the migration of identity and entitlement services onto a MuleSoft-based API shielding layer. Responsibilities include repointing OMES and UPMS away from Salesforce dependencies, developing backward-compatible integrations, executing cutovers with rollback readiness, validating permissions, supporting QA and UAT, migrating entitlement data, and managing Terraform-based infrastructure and deployment pipelines using Golang, Node.js, and Azure.

Develop and maintain complex enterprise Salesforce Service Cloud solutions using Apex, LWC, Flows, REST and Bulk APIs. Support AI-enabled workflows, omnichannel routing, case orchestration, communications platforms, and Salesforce-to-data-platform integrations. Maintain legacy Aura and Visualforce components and CI/CD pipelines. Participate in production support and DFR rotations by monitoring platform health, triaging alerts, investigating automation and routing failures, managing incidents, and documenting findings.

Build production-grade agentic AI systems that analyze infrastructure discovery logs, execution results, errors, and failed discoveries. Develop agents for failure classification, troubleshooting, root-cause analysis, corrective-action recommendations, and probe or pattern improvements. Create telemetry data pipelines and API integrations using Python, REST APIs, generative AI, RAG, and vector databases. Apply SDLC practices, CI/CD, automated testing, security scanning, release management, and deployment automation.

Build and support Salesforce Data Cloud and Marketing Cloud solutions, including data ingestion, identity resolution, audience segmentation, customer journeys, automations, triggered communications, personalization, and activation workflows. Integrate Salesforce platforms with enterprise systems, monitor data quality and performance, troubleshoot campaign issues, and document technical solutions while partnering with marketing, CRM, product, analytics, and data teams.

Architect generative AI workflows for cloud configuration and remediation using RAG, vector databases, and LLM APIs. Automate Terraform modules, Rego policies, and Regula test cases; oversee AI-enabled drift detection, discovery, gap analysis, and remediation capabilities. Provide technical leadership for cloud security tooling AI initiatives, design extensible service architectures, shape technology strategy, and mentor engineers across a distributed global team.

Build production-grade AI applications, backend services, AI agents, agentic workflows, and MCP integrations connecting intelligent systems to enterprise tools. Design prompt orchestration, context management, tool calling, memory, and evaluation workflows. Collaborate on scalable AI deployment patterns, optimize reliability, cost, safety, and observability, and support experimentation, releases, code reviews, and production incidents.

Lead a regional cross-functional DevSecOps pod in a follow-the-sun program. Oversee ASPM integrations and AI-enabled secure code scanning, coordinate timezone handoffs, manage technical escalations, conduct secure code reviews, guide infrastructure architecture, and mentor engineers and data scientists in a regulated enterprise environment.

Build backend services primarily with Java and Spring Boot, while supporting an Angular frontend. Design RESTful APIs, optimize complex SQL, and work extensively with PostgreSQL or Oracle and JPA/Hibernate. Lead modernization from database-centric designs to domain-driven architectures, apply object-oriented design patterns, and collaborate with cross-functional squads. Cloud, Docker, CI/CD, loyalty, fintech, and AI-driven development experience are advantageous.

Build agentic generative AI workflows for cloud configuration and remediation. Develop RAG, vector database, and LLM-based automation for configuration baselines, drift detection, discovery, and gap analysis. Generate and validate Terraform modules, Rego policies, and Regula test cases for CI/CD pipelines. Collaborate with globally distributed engineering teams to deliver cloud security automation.

Develop and maintain Java backend services and data-processing components. Build scalable ingestion, transformation, and integration solutions using data platforms such as Spark and Kafka. Collaborate with stakeholders on system design, implementation, testing, and production operations. Improve service performance, reliability, and maintainability; troubleshoot application and data-layer issues; and contribute to documentation, code reviews, and engineering best practices in an Agile environment.