Senior AI Software Engineer

We need to output HTML fragment with sections: headings

for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Use

• for any list of duties/requirements/benefits, and

  for prose. Do NOT repeat job title as heading. Do NOT put Job ID/requisition numbers as headings. Remove boilerplate, navigation, legal, EEO, equal-opportunity, scam-warning, duplicated lines, application instructions. Include a section only when posting has real specific content for it. Avoid filler like "commensurate with experience", "not specified", etc. Prefer few substantive sections. We need to extract content: About Placer.ai (company description) maybe not needed as "About the role"? The posting includes "About Placer.ai" then "Summary". The role description is after Summary. We can treat "About the role" as the summary paragraph: "Placer.ai is building out PlacerX...". That is substantive. Responsibilities: list of bullet points under "Responsibilities". We need to capture each bullet as

• . The responsibilities list includes many items: Agent Design & Orchestration, The Tool & Context Layer (incl. MCP Servers), MCP Server Development, Auth Infrastructure, Connector Standards, Integration Triage & Prioritization, Plugin & Skill Development, Data Platform Engineering (Databricks / Internal BI), Usage Tracking & Insights, Platform Infrastructure & Cost Optimization, Security & Compliance. Each bullet has description after colon. We'll need to preserve all substantive info. Could format each bullet as
• containing the heading and description? Probably each bullet as a line: "Agent Design & Orchestration Architect agents that ...". We'll keep as is. Requirements: list under "Requirements". Items: 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). We'll list each as
• . Nice To Haves: maybe we can treat as Skills? Could be a Skills section. The posting includes "Nice To Haves". We could put under Skills. It includes: 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. We'll include as
• under Skills. Benefits: The posting mentions "Excellent benefits", "Competitive salary", "Fully remote", "Medical, dental and vision coverage as well as flexible time off, 401K and equity awards for certain roles." Also "U.S. annualized pay range for this position is $160,000 - $190,000 USD." So we have Pay and Benefits sections. Pay: we have salary range. So we need a Pay section with that info. Benefits: list of benefits: medical, dental, vision coverage, flexible time off, 401K, equity awards (for certain roles), fully remote, competitive salary (maybe part of pay). We'll include as
• . Schedule: Not explicitly mentioned; maybe hybrid role? It says "This is a hybrid role". That could be schedule: hybrid (part remote, part office?). But they also say fully remote under WHY JOIN PLACER.AI? Actually they say "Fully remote" under WHY JOIN PLACER.AI? Wait: Under WHY JOIN PLACER.AI? they list: Join a rocketship! ... Competitive salary Excellent benefits Fully remote. So schedule could be "Fully remote". But they also said hybrid role earlier. There's some conflict. The posting says: "This is a hybrid role: part platform engineer, part internal-facing integration lead." That's describing role nature, not work location. Then later under WHY JOIN PLACER.AI? they list "Fully remote". So schedule could be "Fully remote". We'll include Schedule section with "Fully remote". However we need to avoid filler like "not specified". It's specified. We must not include boilerplate like EEO, drug-free workplace, etc. Also remove "Noteworthy Links To Learn More About Placer" etc. We must not repeat job title as heading. So no heading for Senior AI Software Engineer. We'll output HTML fragment with sections: maybe About the role (

  About the role

  ...)

  , Responsibilities (

  Responsibilities

  ...), Requirements (

  Requirements

  ...), Skills (

  Skills

  ...), Benefits (

  Benefits

  ...), Pay (

  Pay

  ...)

  , Schedule (

  Schedule

  ...)

  . Check for any duplicated content: Ensure we don't include "About Placer.ai" company description? Could be part of About the role? Might be considered background but not required. The instruction: "Prefer a few substantive sections over many thin ones." Could include About the role as summary. The company description may be considered filler but it's substantive about the company. However the instruction says "use

  for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." So About the role is a heading. It likely expects a paragraph describing the role, not the company. The company description may be considered part of About the role? Might be okay to include a brief summary of the role (the Summary paragraph). We'll use that. Thus About the role paragraph: "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." That is the summary. Now Responsibilities list: We'll extract each bullet. Let's list them exactly as in posting: - 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, 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. - MCP Server Development 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. - Auth Infrastructure 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. - 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 do the same. - Integration Triage & Prioritization 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. - Plugin & Skill Development 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. - Data Platform Engineering (Databricks / Internal BI) 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. - Usage Tracking & Insights 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. - Platform Infrastructure & Cost Optimization 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. - Security & Compliance 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. Note there is some duplication: "MCP Server Development" repeats similar content as earlier bullet. We'll keep both as separate bullets? Might be considered duplicate. But we should preserve all substantive information; duplicates may be okay but we could deduplicate? The instruction: "Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions." So we should remove duplicated lines. The