Jobs at AgileEngine

Architect generative AI workflows for cloud configuration and remediation using RAG, vector databases, and LLM APIs. Automate Terraform modules, Rego policies, and Regula tests while developing AI-enabled cloud security automation, drift detection, discovery, and remediation capabilities. Lead AI enablement strategy, design extensible service architectures, and mentor engineers across a distributed global team.

Build agentic generative AI workflows for cloud configuration and remediation. Develop RAG and vector database solutions using LLM APIs, automate baseline creation, drift detection, discovery, and gap analysis, and generate compliant Terraform modules, Rego policies, and Regula test cases for CI/CD pipelines. Collaborate with globally distributed engineering teams.

Develop and maintain Java backend services and data-processing components supporting ingestion, transformation, integration, and production operations. Design scalable solutions, improve performance and reliability, troubleshoot application and data issues, and collaborate with engineering and business stakeholders. Contribute to testing, documentation, code reviews, and Agile engineering practices. The role requires experience with Java, data-oriented systems, ETL, Spark, Kafka, APIs, databases, and distributed application design.

Develop and optimize Salesforce Data Cloud and Marketing Cloud solutions, including data ingestion, profile unification, segmentation, journeys, automations, triggered sends, and omnichannel campaigns. Build integrations, monitor platform interoperability, support testing and releases, resolve production issues, and ensure security, compliance, governance, documentation, and scalable platform best practices.

Leads end-to-end technical delivery across Salesforce, AI agents, integrations, and enterprise workflows. Partners with stakeholders and engineering teams on scope, architecture, priorities, and implementation. Builds AI-enabled workflows using Claude and Agentforce, manages delivery risks and dependencies, reviews technical designs and code, mentors engineers, and ensures solutions meet governance, security, reliability, and supportability standards.

Build production-grade AI applications, backend services, agents, agentic workflows, and MCP integrations using Java and/or Python. Integrate LLMs with enterprise tools, implement prompting, context management, tool calling, memory, evaluation, safety, and reliability patterns. Collaborate cross-functionally on scalable AI architectures, troubleshoot production issues, improve observability and efficiency, support releases, and document engineering best practices.