Job Description

Overview:

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.
Responsibilities:

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.


Qualifications:

BTech, BE, MCA
Essential skills:

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.


Desired skills:
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.

Experience:

10-15 Years Experience