Responsibilities

Build and operate the internal application platform that lets employees create and deploy AI-generated applications on curated backend and frontend frameworks, with starter templates, design guidance, authentication, and secure deployment built in, so app authors own only business logic, user flows, and UI.
Give those applications governed access to data, including connectivity to our data warehouse and a shared Postgres cluster for application state that is automatically integrated into our data platform.
Build and operate a central knowledge platform over unstructured content (documents, slide decks, meeting transcripts, Slack) and structured data from battery test stands, manufacturing robots, and deployed systems, pairing document retrieval with query access to structured data through semantic models and text-to-SQL. Own its retrieval quality through semantic and hybrid search, re-ranking, chunking, and classification, with source-level permissions and evaluation sets that show whether a change helps.
Design and deploy the shared MCP infrastructure, including a gateway layer (such as agentgateway) for authentication, authorization, rate limiting, and audit logging, and build MCP servers for applications and services across the software org and for selected external tools.
Own model selection and cost: evaluate open-source and open-weight models against frontier models on our own tasks, decide where each fits, and reduce token usage through context management, caching, and model routing.
Build the agent orchestration layer, including multi-agent workflows and long-running loops with checkpoints, human review gates, and automated evaluation. Work with data engineers to decide when work should run as a deterministic, scheduled workflow and when it needs a graph or state machine in which an agent chooses among bounded steps.
Partner with product R&D, battery design, manufacturing process engineering, product costing, and root cause analysis teams to find repeatable work and automate it, including agent workflows whose output informs critical design and process decisions.
Instrument the platform with tracing, evaluation, and usage and cost attribution by team, and stand up the AWS infrastructure and infrastructure-as-code (Pulumi and/or Terraform) it runs on.
Help stand up a company-wide skills and plugin marketplace that vets contributions against AI policy and scans them for security vulnerabilities with open-source and commercial tools. Advise on AI governance, recommending changes that make the policy specific to platform needs and automating its execution, such as approvals for new tools.
Raise the engineering bar through design docs and RFCs, code review, mentorship, and technical influence that reaches beyond the immediate team.
What you'll bring:
Bachelor's degree in computer science, software engineering, computer engineering, or a related field plus 7+ years of software or data engineering experience, including building and operating production platforms or services that other engineers depend on.

Staff AI Platform Engineer at Formenergy · LumiJobs

Staff AI Platform Engineer

Responsibilities

Qualification

Preferred

Benefits

Company

Formenergy

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