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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff AI Platform Engineer - **Company:** Form Energy - **Location:** Weirton, WV, United States - **Experience:** Expert - **Salary:** $128,660.0 - $184,966.0 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Sql Data Warehouse, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Business Logic, Computing Platforms, Audit Trail, User Authentication, Code Review, Computer Engineering, Continuous Integration, Information Engineering, Data Security, Data Warehousing, Document Retrieval, Key Management, PostgreSQL, OAuth, Open Source Technology, OpenID, Software Engineering, Software Systems, SQL Databases, Workflow Management Systems, Pulumi, ReactJS, Retrieval-Augmented Generation, Large Language Models, Snowflake, Multi-Agent Systems, Software Security, State Machines, Caching, Backend, Agentic-AI, Rate Limiting, Data Layers, AI Platforms, Information Technology, Front End Software Development, Api Design, Model Context Protocol, Terraform, Databricks - **Published:** October 23, 2026 - **Apply:** https://jobs.localjobnetwork.com/apply/add/88699700/1 ## About the Role * 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. * Strong production programming in Python and Go, with experience building APIs and backend services. * Hands-on experience building LLM-powered systems in production, including retrieval-augmented generation, embeddings, vector and hybrid search, re-ranking, and structured-data access through text-to-SQL or semantic layers. * Experience with agent frameworks, tool calling, and the Model Context Protocol, and with workflow orchestration or state-machine frameworks such as LangGraph, Temporal, or Dagster. * Experience building evaluation sets and harnesses for LLM systems and measuring quality, latency, and cost across model providers, including open-weight models. * Experience with PostgreSQL and a cloud data warehouse, plus AWS, containers, CI/CD, and infrastructure-as-code with Pulumi and/or Terraform. * Working knowledge of platform and AI security: authentication and authorization (OAuth/OIDC), secrets management, sandboxed execution, tenant isolation, supply-chain scanning, and risks specific to LLM tools such as prompt injection and data exfiltration. * A track record of building internal platforms that other teams adopt, and of independently leading the design of novel solutions through technical design docs and RFCs. * Excellent communication and organizational skills, including with scientists, engineers, and operators who aren't software developers, and comfort participating in an on-call rotation. * Nice to have: React and design-system experience; Snowflake or Databricks; serving open-weight models (for example with vLLM); API or AI gateway experience; AI governance or compliance frameworks (SOC 2, ISO 27001); background in battery, energy, manufacturing, or other physical-systems data. ## Description * 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.