Job Title : Generative AI Architect Experience : 5 to 9 years Location : Remote Work Hours Engineer 1 : 7 AM 3 PM EST Engineer 2 : 10 AM 6 PM EST Joiner Requirement : Immediate Joiner Preferred Role Overview We are looking for a hands-on Generative AI Architect with strong experience in designing, developing, and deploying scalable AI/ML infrastructure, focusing on LLMs, MLOps, cloud-native architectures, and automation-first platforms. The ideal candidate will work closely with cross-functional teams to implement and optimize large-scale AI solutions in a secure and compliant environment. 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 (ref:hirist.tech)
