Sr. AI Platform Engineer

Duration: Permanent/Direct Hire

Compensation: $150-175k Base + Bonus + Benefits

Location: Fort Collins, CO (on-site)

Remote Eligibility: CA, DC, FL, GA, IL, IN, MN, MS, NC, NV, NY, OH, OR, PA, SC, TN, TX, VA, and WA

 

Responsibilities:

• 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, orchestration, state/context management, and human-in-the-loop approvals.
• 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 that allow engineering teams to consume AI platform capabilities independently.
• 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.

 

Requirements:

• 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.

 

Nice to Have:

• 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.

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