Senior AI Software Engineer Job Buffalo area,New York USA,Software Development

Placer.ai is building out PlacerX, 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 Software 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
Agent Design & Orchestration 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
The Tool & Context Layer (incl. MCP Servers) 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, Data wrapper, 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
MCP Server Development Design, build, and deploy MCP servers across two tracks: external SaaS integrations (Google Analytics, Search Console, Data wrapper, 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
Auth Infrastructure Build the OAuth and credential management layer that makes connector auth work securely lement reusable patterns R&D Architecture can approve and other connectors can adopt
Connector Standards 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…