AI Infrastructure Strategist

2 weeks ago

Delhi, Delhi, India beBeeGenerative Full time US$ 2,00,000 - US$ 2,50,000

Job Title : Generative AI Architect

  • Experience : 5 to 9 years
  • Location : Remote
  • Work Hours :

We are seeking a hands-on expert in designing, developing, and deploying scalable AI/ML infrastructure.

Key Responsibilities :

  1. Design and architect Generative AI and Large Language Model (LLM) based solutions in cloud and hybrid environments.
  2. Build and optimize scalable Machine Learning Operations (MLOps) pipelines using modern tools and frameworks.
  3. Collaborate with data scientists, machine learning engineers, and DevOps teams to productionize AI/ML models.
  4. Ensure infrastructure scalability, observability, and security for AI workloads.
  5. Implement best practices for version control, model governance, and Continuous Integration/Continuous Deployment (CI/CD) for ML pipelines.
  6. Develop automation and self-service capabilities to support rapid experimentation and model deployment.
  7. Advocate and implement Infrastructure-as-Code (IaC) and GitOps workflows using Terraform and Git.
  8. Integrate responsible AI frameworks and enforce policy-as-code for ethical AI deployment.
  9. Drive platform improvements with a focus on developer experience and enablement.
  10. 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 expertise with LLMs, Transformers, or Generative AI frameworks.
  • Deep knowledge of 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.

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