Senior AI Platform Engineer

Placer.ai

4d•$180,000 - $200,000•Remote

About The Position

Placer.ai is building out the AI agentic and context layer we use to run the company internally — the tooling, integrations, agents, and automation that make every team faster. This is the connective tissue of how an AI-native company operates, and we're building it from the ground up. As a Senior AI Platform Engineer, you'll help build that platform end to end — the agents, connectors, automation, and infrastructure that let the entire company unlock major productivity gains. You'll be designing and shipping AI that takes real work off people's plates, turning manual processes into automated ones, and building the systems that let teams do their best work faster. Developing our use of Claude and Databricks — currently the backbone of that internal AI stack — is a key part of the mandate. Every one of our 500+ team members already uses Claude and BI in Databricks for analysis, automation, and research, and demand for deeper integrations is growing faster than our R&D team can deliver. You'll be tasked with driving these capabilities and impact to the next level — from surface-level usage to genuinely agentic, high-leverage workflows embedded across the business. This is a hybrid role: part platform engineer, part internal-facing integration lead. You'll report to the COO and partner across AI Operations, R&D, Data Science, GTM, and other teams, owning the path from integration request to production-grade system. You'll also help build the triage and review process so the broader org can self-serve safely. If you want to build the AI backbone of a fast-moving company — and see your work adopted by hundreds of people within days rather than quarters — this is that role.

Requirements

8+ years of backend engineering experience
Prior experience with MCP servers, LLM tool use, or AI agent frameworks
Prior experience in data engineering or analytics tooling
Solid understanding of REST APIs, OAuth 2.0, and credential management, including Google Cloud auth patterns (gcloud, service accounts)
Experience building and deploying services to Kubernetes or equivalent container infrastructure
Familiarity with Databricks or similar DW/DLs is a plus
Comfort working without an existing playbook — AI platform work at Placer is new; role definition, standards, and tooling will evolve
Strong communication skills; you'll regularly translate between business requests and engineering requirements
Comfortable navigating competing priorities across R&D Architecture (standards), AI Enablement (rollout), and business teams (requests)

Nice To Haves

Demonstrated use of AI tools to work more efficiently—whether professionally or personally—and a curiosity for finding new ways to apply them.
Comfort integrating generative AI into day-to-day workflows to boost productivity, quality, and output.

Responsibilities

Architect agents that don't just answer questions but do work — chaining reasoning, tool calls, and decision-making into reliable multi-step workflows (e.g. pulling data, transforming it, drafting output, and taking action), with the planning and control logic to run those loops autonomously or with a human in the loop. This is the core of turning "a person does this task repeatedly" into "an agent does this task reliably," across every function.
Give agents secure, governed access to the systems and data they need to act — via MCP servers, function/tool interfaces, and retrieval over internal knowledge — so an agent can operate across finance, GTM, Ops, and R&D tools rather than in a sandbox. This is the connective layer that makes autonomous work possible in the real business, not just in a demo. In practice, this means designing, building, and deploying MCP servers across two tracks: external SaaS integrations (Google Analytics, Search Console, Datawrapper, Infogram, and others), where you'll harden community or official MCPs or build from scratch; and fully custom MCPs for Placer's internal tools, owning the path from data source through to the Cowork plugin surface.
Design, build, and deploy MCP servers across two tracks: external SaaS integrations (Google Analytics, Search Console, Datawrapper, Infogram, others), where you'll harden community or official MCPs or build from scratch; and fully custom MCPs for Placer's internal tools, owning the path from data source through to the Cowork plugin surface.
Build the OAuth and credential management layer that makes connector auth work securely at scale. Implement reusable patterns R&D Architecture can approve and other connectors can adopt.
Work with R&D Architecture to define what "production-ready" means for a Placer MCP server — security posture, deployment target (k8s), logging, access controls. Then apply those standards consistently and help others do the same.
Triage incoming connector requests and bug reports from across the company. Distinguish rollout-critical from backlog, and help teams understand what they can self-serve vs. what needs engineering bandwidth. This is request and bug triage, not product roadmap ownership.
Build and maintain Cowork plugins and Claude skills that extend Claude's capabilities for specific Placer workflows — particularly for high-leverage teams in Marketing, Operations, and Data. This includes automating manual, repetitive processes so teams can hand more of their routine work to Claude.
Take ownership of Placer's internal BI environment on Databricks — cleaning up and restructuring the existing data so it's reliable and well-organized, engineering pipelines to bring new data in, and getting it into a state where our internal AI can actually use it. Much of an agent's usefulness depends on the quality of the data underneath it, so making this BI layer AI-ready is foundational to the whole platform.
Build and maintain tooling to track how internal AI tools are being used across the company, so leadership can measure adoption, identify high-value use cases, and make informed decisions about expanding access and investment.
Optimize compute, storage, and networking across the internal AI platform for both performance and cost efficiency — making sure the systems scale with adoption without runaway spend.
Implement security best practices for AI platforms, including identity management, encryption, and compliance monitoring — ensuring that company-wide AI access is safe, auditable, and aligned with our data-handling standards.