Generative AI Platforms Architect

EPAM Systems; Turkey; On-site; TRY 900,000 - 1,500,000; Full time

Job summary

EPAM Systems is seeking a Generative AI Platforms Architect to lead the enterprise GenAI platform architecture across multiple clouds. You will design reference architectures, guardrails, and roadmaps while mentoring engineers in LLMOps/ModelOps and ensuring security, compliance, and responsible AI practices.

You will collaborate with security, data governance, and product teams to translate business goals into scalable platform designs and delivery plans, driving efficiency and best practices.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent experience.
• 1+ years in architecture roles involving cloud, data, or production Generative AI/LLM systems.
• Knowledge of cloud platforms (Azure, AWS, GCP) and IaC tools (Terraform, Bicep, CDK, CloudFormation).
• Proficiency in CI/CD and release management for ML/LLM workloads (Jenkins, GitHub Actions, GitLab CI, Azure DevOps).
• Understanding of LLM platforms like Azure AI Foundry, Azure OpenAI, AWS Bedrock, or Vertex AI.

Responsibilities

• Design enterprise generative AI reference architectures, blueprints, and reusable patterns.
• Define multi-cloud platform foundations addressing networking, identity, and secrets management.
• Lead efforts in LLMOps/ModelOps, focusing on model evaluation, safety, observability, and rollout strategies.
• Create frameworks and governance for agentic systems, including tool governance and MCP.
• Ensure systems comply with security, risk, and compliance standards with Responsible AI and PII controls.
• Establish strategies for cost efficiency, reliability, and performance using capacity planning and FinOps techniques.
• Improve developer experience through CI/CD pipelines, golden paths, templates, and IaC techniques.
• Collaborate with cross-functional teams to align system requirements with strategic objectives.

Skills

Generative AI platforms

Cloud architecture

LLM/ModelOps

CI/CD for ML workloads

Infrastructure as Code

Security & compliance

Cross-functional collaboration

English proficiency

Education

Bachelor’s degree in Computer Science or Engineering

Tools

Terraform

CDK / CloudFormation

Bicep

Kubernetes

Docker

Jenkins

GitHub Actions

GitLab CI

Azure DevOps

Job description

We are seeking a skilled Generative AI Platforms Architect to lead and define the architecture for our enterprise GenAI platform. The role involves creating reference architectures, guardrails, and roadmaps to deliver scalable and secure AI solutions, including LLMs, agentic applications, and tool integrations, across multiple clouds. By collaborating with engineering, security, data governance, and product teams, the Architect will translate business goals into platform designs and actionable delivery plans, while mentoring engineers and advocating for best practices in LLMOps/ModelOps.

Responsibilities

• Design enterprise generative AI reference architectures, blueprints, and reusable patterns
• Define multi-cloud platform foundations addressing networking, identity, and secrets management
• Lead efforts in LLMOps/ModelOps, focusing on model evaluation, safety, observability, and rollout strategies
• Create frameworks and governance for agentic systems, including tool governance and the Model Context Protocol (MCP)
• Ensure systems comply with security, risk, and compliance standards, with Responsible AI principles and PII controls applied
• Establish strategies for cost efficiency, reliability, and performance using capacity planning and FinOps techniques
• Improve developer experience through CI/CD pipelines, golden paths, templates, and Infrastructure as Code (IaC) techniques
• Collaborate with cross-functional teams to align system requirements with strategic objectives

Requirements

• Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent experience
• 1+ years in architecture roles involving cloud, data, or production Generative AI/LLM systems
• Knowledge of cloud platforms (Azure, AWS, GCP) and IaC tools (Terraform, Bicep, CDK, CloudFormation)
• Competency in containerization and orchestration technologies (Docker, Kubernetes), as well as API gateways/service meshes
• Proficiency in CI/CD and release management for ML/LLM workloads (Jenkins, GitHub Actions, GitLab CI, Azure DevOps)
• Understanding of Large Language Model (LLM) platforms like Azure AI Foundry, Azure OpenAI, AWS Bedrock, or Google Vertex AI
• Familiarity with security-by-design principles and compliance frameworks tailored to GenAI systems
• Advanced proficiency in English (B2+/C1)