BOOMI Integration Engineer

Company Overview

Position Overview

Responsibilities

• Embrace and drive a culture of accountability for engineering-leverage and adoption outcomes. Build platform capabilities that solve recurring problems once, well, for many teams—reducing per-team build and operate toil while ensuring consistency and compliance by default through high-quality, lean designs and implementations.
• Serve as the technical advocate for the platform as a product, ensuring capability integrity, feasibility, and alignment with the needs of the engineering teams who consume it. Lead requirement discovery with consuming teams, component design of platform services, frameworks, and the AI control plane, and their development, testing, integration, and support.
• Maintain accountability for the integrity of the platform architecture and for the enterprise tech-stack conformance baseline that engineering teams build against. Manage platform dependencies, code design, implementation, the data and policy-as-code enforcement layers, and the OpenTelemetry-based instrumentation substrate—building capabilities that are operable, instrumented, performant, and drift-resistant by design, to the production standards set by SRE. Stay hands-on, self-driven, and continuously learn new approaches, languages, and frameworks. Create technical specifications, codify recurring patterns into reusable components and golden paths, and write high-quality, supportable, scalable code to ensure all platform KPIs (adoption, reliability-by-design, and developer experience) are met or exceeded. Demonstrate collaborative skills to work effectively with diverse teams.
• Develop lean platform capabilities through rapid, inexpensive experimentation to solve the real needs of consuming engineering teams. Engage with those teams before, during, and after delivery to ensure the right capability is delivered at the right time—and adopted, not shelved.
• Adopt a mindset that favors action and evidence over extensive planning. Utilize a leaning-forward approach to navigate complexity and uncertainty, delivering platform capabilities as lean, adoption-validated increments rather than big-bang builds, and keeping them supportable and maintainable.
• Work collaboratively with empowered, cross-functional partners: engineering, SRE, security and risk, data governance, and engineering leadership and architecture. Integrate their constraints into the paved roads so that the secure, compliant, and reliable path is the easy path. Deliver capabilities that are admissible-by-design and submit them for admission, co-defining service-level objectives with SRE, who set production standards and own the admission decision. Foster a collaborative environment that enhances team synergy and innovation.

Skills & Experience

• A bachelor’s degree in computer science, software engineering, data science, machine learning, or related discipline. Experience is the most relevant factor.
• Hands On experience with BOOMI Integration is important.
• 5+ years of software and platform engineering experience with most of the following: Angular, React, NodeJS, Python(Mandatory), , C#, .NET, Java, Rust, SQL/NoSQL, PyTorch, TensorFlow, LangChain, LangGraph, LangSmith, LangFuse, Terraform, as well as unit, integration, and end-to-end testing frameworks & tools, specifically BDD, Gherkin, Cucumber, Playwright, and Selenium.
• 3+ years of experience designing, building, and operating AI/ML platform or infrastructure, with hands-on experience across building tooling for MLOps/LLMOps, model serving, retrieval and vector infrastructure, and eval/observability instrumentation for LLM integration (OpenAI, Anthropic, or open-source models).
• 3+ years of experience with cloud-native engineering on any of the cloud hyperscalers such as Azure, AWS, or GCP—including their AI/ML services such as Azure OpenAI, AWS Bedrock, or Vertex AI—as well as container orchestration (Kubernetes, Docker), Big Data, Databricks, CI/CD at platform scale, and distributed systems.
• Prior experience with AI control-plane and agent-runtime patterns: model/LLM gateway, A2A and MCP integration, agent runtimes (e.g., Google ADK, Amazon Bedrock AgentCore), guardrails (PII redaction, prompt-injection, content, tool permissioning/tool-RBAC), policy-as-code, and multi-tenant isolation.
• Prior experience with enterprise data platform engineering: data pipelines, self-service and data-product enablement, governance-as-code enforcement, and metadata/lineage.