Generative AI Architect

Job Title :

Experience :

Location :

Work Hours

• Engineer 1 : 7 AM 3 PM EST
• Engineer 2 : 10 AM 6 PM EST
  

Joiner Requirement :

Role Overview

Key Responsibilities (KRA)

• Architect, design, and deploy Generative AI and LLM-based solutions in cloud and hybrid environments
• Build and optimize scalable and repeatable MLOps pipelines using modern tools and frameworks
• Collaborate with data scientists, ML engineers, and DevOps teams to productionize AI/ML models
• Ensure infrastructure scalability, observability, and security for AI workloads
• Implement best practices for version control, model governance, and CI/CD for ML pipelines
• Develop automation and self-service capabilities to support rapid experimentation and model deployment
• Advocate and implement Infrastructure-as-Code (IaC) and GitOps workflows using Terraform and Git
• Integrate responsible AI frameworks and enforce policy-as-code for ethical AI deployment
• Drive platform improvements with a focus on developer experience and enablement
• Collaborate on system integration with APIs, cloud-native services, and enterprise-grade data platforms
  

Primary Skillsets

• 5+ years of experience in AI/ML infrastructure, MLOps, platform engineering, or cloud architecture
• Proven hands-on experience with Large Language Models (LLMs), Transformers, or Generative AI frameworks
• Deep expertise in building and maintaining Kubernetes-based AI workloads in production
• Strong proficiency in Terraform and Git-based infrastructure workflows (GitOps)
• Experience with popular MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI, etc.)
• Proficient in scripting with Python and Bash for automation and orchestration
• Deep understanding of cloud services across AWS / Azure / GCP (compute, storage, networking, security)
• Knowledge of model security, governance, and responsible AI practices
• Familiarity with container security, identity management, and policy-as-code (OPA, Kyverno)
  

Secondary Skillsets (Preferred)

• Experience with LLM fine-tuning, RAG (Retrieval-Augmented Generation), vector databases (e.g., FAISS, Pinecone)
• Familiarity with prompt engineering and custom model deployment
• Exposure to Microsoft Power Platform or Dynamics 365 integration
• Experience working with Google Cloud AI/ML services
• Familiarity with observability and logging tools like Prometheus, Grafana, ELK/EFK, and OpenTelemetry