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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior IT & AI Systems Engineer - **Company:** Cloudzero Inc. - **Location:** Boston, MA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Systems Engineering, JIRA, Microsoft Azure, Bash Shell, Software as a Service, Information Systems, Information Engineering, Cursor, Human Resources Information System (HRIS), Identity and Access Management, Python (Programming Language), Network Segmentation, Role-Based Access Control, DataOps, Salesforce.Com, Single Sign-On, SQL Databases, Data Streaming, Pulumi, Google Cloud, Okta, Retrieval-Augmented Generation, Claude Code, Snowflake, Guidewire PolicyCenter, Cloudformation, Information Technology, Slack, Apache Kafka, Casper Suite, Gsuite, Claude, Hubspot, Webhooks, Human in the Loop, Surface Modeling - **Published:** October 27, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-it-ai-systems-engineer-boston-ma--fceb5d78-34bd-49c4-818a-249e0d93f672 ## About the Role Systems Engineering, Artificial Intelligence (AI), Information Technology & Information Systems, Artificial Intelligence (AI), Software as a Service (SaaS), Surface Modeling, Slack, Atlassian JIRA, Laptop PC, Automation, * 7+ years at the intersection of data engineering and infrastructure. You've built pipelines and the platforms they run on, and been on call for both. * Working Snowflake experience, or a comparable warehouse you've built pipelines into and governed. Familiarity with its RBAC, policy, and cost model, or a clear track record of picking that up on a new platform. * Comfortable with orchestration (Airflow, Dagster, Prefect, or equivalent) and opinions about idempotency, backfills, and late-arriving data. Transformation tooling like dbt is a plus, not a prerequisite. * Strong software engineering fundamentals. Python required, SQL assumed, Go or Bash a plus. IaC at scale (Pulumi, CDK, CloudFormation) where you set the standard rather than follow it. * Hands-on production AI and LLM experience with agents, RAG, tool-calling, and MCP or equivalent, plus a point of view on agent identity, tool governance, and what breaks once it's live. * Deep AWS (Bedrock, IAM, EventBridge, Lambda) with working knowledge of GCP and Azure. Strong API instincts: you've stitched SaaS systems together with REST, webhooks, and event hooks, and know where those integrations rot. * Working command of the IT toolkit: Okta SSO and Workflows, Jamf including packaging, Google Workspace, and Jira, plus experience automating employee lifecycle against an HRIS. * A root-cause mindset and a bias for shipping. You're exceptional with people. This role sits close to every team, and how you make someone feel matters. Nice to Have * Streaming or event-driven data experience (Kafka, Kinesis, Snowpipe) * Data observability and lineage tooling in production * Experience evaluating retrieval quality, where you measured whether RAG actually worked rather than just shipping it * Practical familiarity with SOC 2 or ISO 27001 * Examples of agents, pipelines, or automations that retired a recurring class of work ## Description Manual work shows up in two shapes here, and they're the same problem: * A ticket is a signal that a system failed a person. Ask why the question came up at all, and fix the upstream cause so the next ten people don't hit the same wall. * A repeated request for a number is a data product that doesn't exist yet. The third time someone pulls the same figure by hand, that's not a favor to do, it's a table you haven't modeled. The queue and the query log are both data. Instrument them, group by root cause, and let the pattern drive your roadmap. We're AI-Native, For Real * You reach for Claude Code, Claude Desktop, or Cursor before problem-solving manually, whether that's drafting transformations, parsing logs, reasoning about a schema you've never seen, or breaking apart messy projects. * You can talk credibly about which models and tools are good at what, where they fall short, and how to prompt them well. * You try new tools as they show up, and drop them when they don't earn their keep., * Ingestion from our SaaS estate and cloud billing sources. CDC and ELT out of Salesforce/HubSpot, Jira, Okta, Ravenna, UKG, and support tooling, with schema drift handled and backfills that are boring. * The modeled warehouse: conformed dimensions, tested transformations, and a semantic layer where "ARR" resolves to one number regardless of who asks. * Data quality as a product concern: freshness SLAs, drift alerting, lineage. When a pipeline breaks silently, an agent confidently gives a VP the wrong answer. * Governance in the warehouse itself: Snowflake RBAC, row- and column-level policy, and masking mapped to Okta groups, so access is inherited from identity rather than granted by ticket. * Cost visibility per team, per workload, per agent. We sell cost intelligence. Ours should be exemplary. * Data products other teams run on: Marketing attribution, Finance close support, Sales pipeline, and CS health, built as self-serve surfaces rather than a request queue routed through you. * The retrieval layer agents depend on: chunking strategy, embedding pipelines, index freshness, and evaluation of retrieval quality. A stale index is a wrong answer with confidence. * The identity-inheritance model, so an agent invoked by a CS rep or a finance analyst operates with exactly the permissions they have across AWS, Snowflake, and SaaS. Never more. No shared service accounts. * AI Landing Zones across AWS, GCP, Azure, and Snowflake: governed, self-service environments where any department can deploy agents safely without being cloud engineers. * The developer experience for internal agent builders: templates, deploy paths, docs, and office hours that turn one team's work into every team's capability. The systems underneath * The core IT platform (Okta, Jamf, Google Workspace, Slack, Jira, Ravenna) run as a product with a roadmap and a shrinking manual surface. * Employee lifecycle automated end-to-end: joiners, movers, and leavers driven by HRIS as the source of truth, with no human in the loop. * The cloud perimeter: account structure, SCPs, IAM, and network segmentation for our major cloud providers (AWS, Azure, Snowflake), plus a Security partnership where new tooling is safe by default rather than safe by review. What Your First Year Looks Like * Every system of record lands in Snowflake on a schedule people trust, with alerting that catches a break before a stakeholder does. * A modeled, documented core layer exists, and the first three teams outside Engineering answer their own questions against it. * Warehouse access is inherited from Okta groups rather than granted by request. * One agent is in production against that layer, running with its invoker's permissions, with its cost attributed to a team.