Sr. AI Platform Engineer

Posted 27d ago

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Build reusable platform services and frameworks for production AI applications
Establish common patterns for LLMs, RAG, AI agents, and machine learning services
Develop shared APIs, SDKs, libraries, templates, and internal tooling
Build platform capabilities for agentic workflows including tool/function calling and orchestration
Develop reusable RAG capabilities including ingestion, chunking, embeddings, retrieval, ranking, and grounding
Build AI evaluation, guardrails, monitoring, and observability
Support model serving, inference services, vector/search infrastructure, and AI data pipelines
Build CI/CD and deployment patterns for AI applications
Establish LLMOps/MLOps practices for versioning, testing, deployment, monitoring, and rollback
Create self-service tooling and paved paths for engineering teams to consume AI platform capabilities
Design and operate secure, scalable AWS infrastructure for AI workloads
Partner with AI Engineers, Software Engineers, Data Engineers, Data Scientists, Security, SRE, and Product teams
Help establish architecture and engineering standards for production AI

5+ years of software engineering, platform engineering, SRE, DevOps, or cloud infrastructure experience
Strong hands-on AWS experience
Strong Python development experience
Hands-on experience building or supporting production Generative AI / LLM applications
Experience designing and implementing RAG solutions
Experience with embeddings, vector search/vector databases, retrieval, and grounding
Experience with AI agents / agentic workflows, including tool or function calling
Experience with LLMOps/MLOps practices
Experience implementing AI evaluation, guardrails, and observability
Hands-on experience with Kubernetes, containers, and Terraform/IaC
Experience building APIs, services, shared frameworks, or platform capabilities used by multiple engineering teams
Strong understanding of distributed systems, reliability, monitoring, and production operations
Experience working with sensitive or regulated data

Experience with multiple LLM providers and model-routing/model-gateway architectures
Experience with AI orchestration or agent frameworks
Experience with vector databases and enterprise search platforms
Experience with event-driven or real-time architectures
Experience building internal developer platforms or self-service engineering tooling
Experience with fraud, risk, reconciliation, or financial workflow use cases
Fintech, banking, payments, or other regulated-industry experience
Experience with model serving and inference infrastructure
Experience optimizing AI systems for latency, scalability, and cost

Skills