AIML Architect- Hiring For our client in Bangalore

Indihire Consultants; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Indihire Consultants seeks an experienced AI/ML Architect to lead design, deployment, and operation of AI/ML and GenAI solutions on cloud platforms. You will shape architecture with LLMs, RAG, MLOps, and stakeholder collaboration.

The role requires 12+ years IT experience with 8+ years in AI/ML, deep expertise in GenAI, LLMs, and cloud AI services, plus hands-on skills in Python, PyTorch, TensorFlow, and vector databases. The position is full-time in Bengaluru/Pune.

Qualifications

• 12+ years of IT experience with 8+ years in AI/ML.
• 4+ years of experience in AI/ML solution architecture.
• Strong expertise in GenAI, LLMs, RAG, LangChain, and LlamaIndex.
• Experience with Python, PyTorch, TensorFlow, Hugging Face, Pandas, and NumPy.
• Knowledge of NLP, Deep Learning (CNN, RNN, LSTM, Transformers), and unstructured data retrieval.
• Experience with AWS SageMaker, Azure ML, Vertex AI, Docker, and Kubernetes.
• Strong understanding of MLOps/LLMOps, CI/CD, model monitoring, and governance.
• Excellent leadership, communication, and stakeholder management skills.

Responsibilities

• Architect and design scalable AI/ML and GenAI solutions on AWS, Azure, or GCP.
• Design and implement RAG pipelines, LLM-based applications, and AI-driven enterprise solutions.
• Work with LangChain, LlamaIndex, Prompt Engineering, and Vector Databases (Pinecone, FAISS, Elasticsearch, Weaviate).
• Lead ML model development, deployment, monitoring, and MLOps/LLMOps implementation.
• Define AI architecture, technical roadmaps, and cloud deployment strategies.
• Mentor AI/ML teams and drive technical excellence across projects.
• Collaborate with clients and stakeholders to deliver business-focused AI solutions.

Skills

AI/ML architecture

GenAI

LLMs

RAG

LangChain

LlamaIndex

Python

PyTorch

TensorFlow

NLP

MLOps/LLMOps

AWS SageMaker

Azure ML

Vertex AI

Docker

Kubernetes

CI/CD

Model monitoring

Tools

AWS SageMaker

Azure ML

Vertex AI

Docker

Kubernetes

LangChain

LlamaIndex

Job description

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AI/ML Architect

Location: Bengaluru / Pune
Experience: 12+ Years (8+ Years in AI/ML)
Employment Type: Full-Time

Job Summary

We are looking for an experienced AI/ML Architect to lead the design, development, deployment, and operationalization of AI/ML and Generative AI solutions on cloud platforms. The ideal candidate should have strong expertise in LLMs, RAG architectures, MLOps, cloud-native AI solutions, and stakeholder management.

Key Responsibilities

• Architect and design scalable AI/ML and GenAI solutions on AWS, Azure, or GCP.
• Design and implement RAG pipelines, LLM-based applications, and AI-driven enterprise solutions.
• Work with LangChain, LlamaIndex, Prompt Engineering, and Vector Databases (Pinecone, FAISS, Elasticsearch, Weaviate).
• Lead ML model development, deployment, monitoring, and MLOps/LLMOps implementation.
• Define AI architecture, technical roadmaps, and cloud deployment strategies.
• Mentor AI/ML teams and drive technical excellence across projects.
• Collaborate with clients and stakeholders to deliver business-focused AI solutions.

Required Skills

• 12+ years of IT experience with 8+ years in AI/ML.
• 4+ years of experience in AI/ML solution architecture.
• Strong expertise in GenAI, LLMs, RAG, LangChain, and LlamaIndex.
• Experience with Python, PyTorch, TensorFlow, Hugging Face, Pandas, and NumPy.
• Knowledge of NLP, Deep Learning (CNN, RNN, LSTM, Transformers), and unstructured data retrieval.
• Experience with AWS SageMaker, Azure ML, Vertex AI, Docker, and Kubernetes.
• Strong understanding of MLOps/LLMOps, CI/CD, model monitoring, and governance.
• Excellent leadership, communication, and stakeholder management skills.

Preferred

• AWS/GCP/Azure certifications.
• Experience leading enterprise AI transformation programs.

Keywords: AI Architect, ML Architect, Generative AI, GenAI, LLM, RAG, LangChain, LlamaIndex, MLOps, LLMOps, AWS, Azure, GCP, Python, NLP, Machine Learning, Deep Learning.