Requirements

Additional 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)
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.
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.

Senior AI Software Engineer

Summary

Requirements

Additional Requirements

Sign in to see the full job details

Report this Job / Company (রিপোর্ট)

Summary

Requirements

Additional Requirements

Sign in to see the full job details

Report this Job / Company (রিপোর্ট)

Similar jobs