Agentic AI Architect

As an Agentic AI Architect, you will define the technical blueprint for scalable, autonomous AI systems across the enterprise. You’ll integrate Agentic frameworks with LLMOps pipelines, ensuring robustness, compliance, and continuous optimization at scale.

• Key Responsibilities

  • Architect multi-agent ecosystems and tool orchestration frameworks using LangChain, OpenAI Agents SDK, or Google ADK.
  • Design system-level LLMOps and MLOps frameworks (model lifecycle, retraining, observability, feedback loops).
  • Establish CI/CD, monitoring, and scalability strategies for AI microservices.
  • Define model optimization pipelines (quantization, distillation, pruning, caching).
  • Integrate MLflow and Vertex AI for experiment tracking and production deployment.
  • Drive cloud infrastructure strategy — compute scaling, GPU optimization, container orchestration.
  • Ensure AI safety, interpretability, and compliance across models and agents.

BTech, BE, MCA

• Core Skills

  • Architecture Expertise: Agentic systems, reasoning pipelines, and orchestration layers.
  • MLOps / LLMOps: MLflow, Vertex AI Pipelines, Weights & Biases, model monitoring.
  • DevOps Infrastructure: Kubernetes, Docker, Terraform, Jenkins, GCP/AWS CI/CD.
  • Optimization Techniques: Finetuning (LoRA, QLoRA), model quantization, distillation, caching.
  • System Design: Scalable APIs, message queues, observability, and fault tolerance.
  • Programming: Python
  • Cloud Platforms: Google Cloud (Vertex AI), AWS, or Azure.

• Strong understanding of AI safety, governance, and trust frameworks.
• Experience implementing MCP, multi-agent orchestration, or custom reasoning layers.
• Proven success in leading enterprise-scale AI transformation initiatives.

10–15 Years Experience