GenAI/Agent Platform Engineer

SRM Digital; Roanoke (TX); On-site; USD 150,000 - 210,000; Full time

Job summary

SRM Digital is seeking an experienced GenAI / Agent Platform Engineer to design, build, and operationalize scalable GenAI platforms for enterprise-grade AI applications, focusing on secure development, deployment, and integration of LLMs and AI agents.

You will implement RAG pipelines, vector databases, and CI/CD for GenAI workloads, collaborate with data scientists and DevOps, and apply Terraform for IaC, Docker/Kubernetes for containers, and observability with Datadog, Prometheus, or

Qualifications

• 6–10 years of experience in Software/Platform/ML Engineering.

Responsibilities

• Design and build scalable GenAI and Agentic AI platforms for enterprise applications.

Skills

Generative AI

LLMs

Agentic AI

REST APIs

Docker

Kubernetes

Python

LangChain

LangGraph

LlamaIndex

Semantic Kernel

Terraform

CI/CD

Cloud (Azure/AWS)

Vector Databases

Datadog/Prometheus/Grafana

Tools

Terraform

Datadog

Prometheus

Grafana

OpenSearch

Job description

We are seeking an experienced GenAI / Agent Platform Engineer to design, develop, and operationalize scalable platforms for Generative AI, LLM, and Agentic AI applications.

This role will focus on building the foundational platform capabilities that enable development teams to securely develop, deploy, integrate, monitor, and scale GenAI and agent-based solutions within an enterprise environment.

Key Responsibilities

• Design and build scalable GenAI and Agentic AI platforms for enterprise applications.
• Develop platform capabilities supporting LLMs, AI agents, RAG, prompt engineering, model integration, and AI workflows.
• Build and maintain APIs and services for integrating LLMs and AI agents with enterprise applications and data sources.
• Work with LLM platforms and services such as Azure OpenAI, AWS Bedrock, Google Vertex AI, or similar technologies.
• Develop and deploy AI workloads using Python, REST APIs, Docker, and Kubernetes.
• Build reusable frameworks and platform components for multi-agent and agentic AI applications.
• Implement RAG pipelines using vector databases and enterprise data sources.
• Integrate vector databases such as Pinecone, Azure AI Search, OpenSearch, pgvector, or similar technologies.
• Implement AI platform observability, including LLM performance, latency, token usage, errors, quality, and application metrics.
• Establish CI/CD and automated deployment processes for GenAI applications and AI agents.
• Implement Infrastructure as Code using Terraform or similar technologies.
• Develop secure mechanisms for authentication, authorization, secrets management, and API access.
• Support model evaluation, prompt/version management, guardrails, and responsible AI practices.
• Collaborate with data scientists, ML engineers, application developers, architects, DevOps/SRE teams, and business stakeholders.
• Troubleshoot production issues and improve the reliability, scalability, and performance of AI platforms.
• Develop reusable standards, documentation, reference architectures, and best practices for enterprise GenAI adoption.

Required Skills

• 6–10 years of experience in Software Engineering, Platform Engineering, Cloud Engineering, ML Engineering, or related areas.
• Strong hands‑on experience with Generative AI, LLMs, and/or Agentic AI.
• Experience with LLM platforms such as Azure OpenAI, AWS Bedrock, Vertex AI, or equivalent.
• Experience with LLM APIs, prompt engineering, embeddings, RAG, and vector databases.
• Experience developing AI agents / agentic workflows using frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar technologies.
• Strong experience with REST APIs, microservices, Docker, and Kubernetes.
• Experience with AWS and/or Azure cloud platforms.
• Strong knowledge of CI/CD, Git, DevOps, and Infrastructure as Code.
• Hands‑on experience with Terraform or equivalent IaC tools.
• Experience with observability and monitoring tools such as Datadog, Prometheus, Grafana, Splunk, or CloudWatch.
• Strong understanding of cloud security, IAM, API security, secrets management, and enterprise integration.