AI Architect

Teleglobals; Pune District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Teleglobals in India invites an experienced AI/ML architect to design and lead enterprise AI solutions across Classic ML, GenAI, and agentic AI on AWS or Azure. You will define strategy, architecture patterns, and responsible AI practices for customers.

You will design end-to-end architectures, MLops pipelines, and collaborate with data teams to ensure data readiness; lead PoCs, architecture reviews, and workshops.

Qualifications

• 5-10 years of experience in AI or ML architecture, data science, or ML engineering.
• Deep knowledge of ML frameworks (PyTorch, TensorFlow, scikit-learn).
• Hands-on experience with cloud AI or ML services - AWS (SageMaker, Bedrock) or Azure (Azure ML, Azure OpenAI Service).
• Strong understanding of LLMs, foundation models, prompt engineering, RAG architectures, and agentic AI patterns.
• Experience designing MLOps pipelines and model lifecycle management.
• Strong understanding of data preparation, feature engineering, and model evaluation.

Responsibilities

• Design end-to-end AI or ML solution architectures on cloud platforms - AWS or Azure.
• Define AI strategy and roadmaps aligned with customer objectives covering Classic ML, GenAI, and Agentic AI.
• Architect GenAI solutions using foundation models, RAG patterns, fine-tuning, and multi-agent or agentic frameworks.
• Design agentic AI architectures including tool use, planning, memory, and orchestration.
• Design MLOps pipelines for model training, evaluation, deployment, and monitoring.
• Evaluate and recommend AI or ML services, frameworks, and tools for specific use cases.
• Lead technical workshops, PoCs, and architecture reviews for AI initiatives.
• Collaborate with Data Architects and Data Engineers to ensure data readiness for AI workloads.

Skills

AI architecture

ML frameworks

Cloud platforms

LLMs & GenAI

MLOps

Data readiness

Prompt engineering

Education

Bachelor's degree in CS/EE

Tools

AWS SageMaker

Azure ML

TensorFlow

PyTorch

Bedrock

LangChain

Kubernetes

Job description

Design and lead the implementation of enterprise AI solutions - spanning Classic ML, Generative AI, and
Agentic AI - on AWS or Azure, defining AI strategy, architecture patterns, and responsible AI practices for
customers.

Key Responsibilities

• Design end-to-end AI or ML solution architectures on cloud platforms - AWS (SageMaker, Bedrock, Rekognition, Comprehend) or Azure (Azure ML, Azure OpenAI Service, Cognitive Services)
• Define AI strategy and roadmaps aligned with customer business objectives covering Classic ML, GenAI, and Agentic AI
• Architect GenAI solutions using foundation models, RAG patterns, fine-tuning strategies, and multiagent or agentic frameworks
• Design agentic AI architectures including tool use, planning, memory, and orchestration patterns
• Design MLOps pipelines for model training, evaluation, deployment, and monitoring
• Evaluate and recommend AI or ML services, frameworks, and tools for specific use cases
• Lead technical workshops, PoCs, and architecture reviews for AI initiatives
• Collaborate with Data Architects and Data Engineers to ensure data readiness for AI workloads

Required Qualifications

• 5 - 10 years of experience in AI or ML architecture, data science, or ML engineering
• Deep knowledge of ML frameworks (PyTorch, TensorFlow, scikit-learn)
• Hands-on experience with cloud AI or ML services - AWS (SageMaker, Bedrock) or Azure (Azure ML, Azure OpenAI Service)
• Strong understanding of LLMs, foundation models, prompt engineering, RAG architectures, and agentic AI patterns
• Experience designing MLOps pipelines and model lifecycle management
• Strong understanding of data preparation, feature engineering, and model evaluation

Preferred Qualifications

• Cloud certification - AWS Machine Learning Specialty or Azure AI Engineer Associate
• Experience with multi-agent AI systems and agentic orchestration frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI, Amazon Bedrock Agents)
• Knowledge of responsible AI frameworks and AI governance