Artificial Intelligence Engineer

MatchPoint; United States; On-site; USD 170,000 - 230,000; Full time

Job summary

MatchPoint is seeking a senior AI Platform Engineer to design, build, and operate enterprise-scale AI platforms for GenAI, LLMs, RAG, ML, and agentic workloads. This is a hands-on platform engineering role focused on production infrastructure used by multiple AI teams.

You will architect and maintain Kubernetes-based infrastructure across cloud providers, implement MLOps, and build reusable platform services while ensuring security, observability, and reliability.

Qualifications

• Hands-on experience building production AI infrastructure or AI platforms.

Responsibilities

• Build and operate scalable AI/ML platforms and infrastructure for enterprise GenAI and LLM workloads.
• Design gateways, model serving/inference, and API layers for reusable AI services.
• Develop platform capabilities for RAG, vector search, embeddings, retrieval, and enterprise knowledge access.
• Automate infrastructure using Terraform / IaC.

Skills

AI Platform

Kubernetes

Terraform

MLOps

Python

Go

Cloud (AWS/Azure/GCP)

Observability

IAM/RBAC

LLM Serving

Tools

KServe

NVIDIA Triton

Ray Serve

MLflow

ArgoCD

Prometheus

Grafana

OpenTelemetry

vLLM

Job description

*Must be able to work without VISA sponsorship/VISA transfer*

We are looking for a senior AI Platform Engineer to design, build, and operate enterprise-scale AI platforms supporting GenAI, LLMs, RAG, ML, and agentic AI workloads.

This is a hands-on platform engineering role — not a pure AI application development, data science, or architecture position. The ideal candidate has built, and operated production AI/ML infrastructure used by multiple engineering or AI teams.

What You'll Do

• Build and operate scalable AI/ML platforms and infrastructure for enterprise GenAI and LLM workloads
• Design LLM gateways, model serving/inference, model routing, API layers, and reusable AI services
• Build platform capabilities for RAG, vector search, embeddings, retrieval, reranking, and enterprise knowledge access
• Develop and maintain Kubernetes-based AI infrastructure across AWS, Azure, and/or GCP
• Automate infrastructure using Terraform / Infrastructure as Code
• Build CI/CD and GitOps pipelines for AI/ML workloads
• Implement MLOps/LLMOps, including model/prompt versioning, evaluation, deployment, monitoring, and rollback
• Build observability for latency, throughput, token usage, cost, errors, availability, and AI quality
• Develop reusable APIs, SDKs, templates, and platform services that enable multiple AI teams
• Implement enterprise security, IAM/RBAC, secrets management, data access controls, and governance
• Troubleshoot production issues, improve reliability, and establish SLOs/SLIs and operational best practices
• Strong hands-on experience building AI/ML platforms or AI infrastructure
• Terraform / IaC
• AWS, Azure, and/or GCP
• LLM serving, inference, model routing, or LLM gateways
• MLOps / LLMOps
• Python and/or Go
• CI/CD and GitOps
• AI/ML observability and production operations
• Experience building reusable platform capabilities for multiple teams

Experience with technologies such as vLLM, KServe, NVIDIA Triton, Ray Serve, MLflow, ArgoCD, Prometheus, Grafana, OpenTelemetry, Azure OpenAI, AWS Bedrock, or Vertex AI is highly desirable.

We are especially interested in engineers from FAANG/Big Tech or leading AI companies who have worked on production-scale AI infrastructure, ML platforms, model serving, distributed systems, or LLM platforms.

Not the right fit: candidates whose experience is primarily prompt engineering, data engineering, data science/research, AI application development, or consuming existing AI platforms without building the underlying infrastructure.