AI Practice Lead

CloudAI Technologies; Hyderabad; Hybrid; INR 4,000,000 - 8,000,000; Full time

Job summary

CloudAI Technologies is looking for an AI Practice Lead to build and lead our AI engineering and Generative AI practice. This is a senior technical leadership role combining AI architecture, hands-on leadership, solution development, client engagement, team building, and practice development.

The AI Practice Lead will define how CloudAI designs and delivers enterprise AI solutions, including Generative AI, agentic AI, RAG, enterprise search, ML, intelligent automation, and AI-enabled

Qualifications

• 10+ years in technology with recent AI/ML delivery.
• Hands-on with Generative AI and LLM app architecture.
• Strong Python development skills.
• Production apps on major LLM platforms and APIs.
• Experience with RAG, embeddings, vector search, and enterprise data systems.
• Solid ML fundamentals and cloud data architectures.
• Ability to lead teams and engage clients.

Responsibilities

• Define AI practice strategy, architecture standards, engineering methodologies, and technology roadmap.
• Architect enterprise AI and Generative AI solutions across AWS, Azure and modern AI platforms.
• Lead development of LLM-powered applications, RAG systems, AI agents, multi-agent workflows, copilots, and intelligent automation solutions.
• Design enterprise AI architectures covering models, data, vector/search infra, APIs, orchestration, security and observability.
• Evaluate foundation models, AI platforms, frameworks, and emerging tech based on requirements.
• Establish patterns for prompt engineering, context engineering, tool calling, agent orchestration, model eval, and guardrails.
• Define approaches for RAG, embeddings, vector search, knowledge retrieval, and data integration.
• Establish LLMOps/MLOps, evaluation, monitoring, governance, and responsible AI practices.
• Lead technical discovery and architecture workshops with customers.
• Translate business requirements into practical AI solutions and roadmaps.
• Develop prototypes and proofs of concept; guide production-grade implementations.
• Provide architecture and code reviews for critical AI implementations.
• Build reusable AI accelerators, frameworks, reference architectures, and components.
• Mentor AI engineers, data engineers, and software engineers.
• Participate in hiring and capability development for the AI practice.
• Support proposals and technical discussions with customers.
• Partner with CloudAI's Cloud, Data, and Application practices to deliver integrated solutions.

Skills

Generative AI architecture

Python development

LLM API integration

RAG and embeddings

AI security and governance

Tools

AWS

Azure

Azure OpenAI

Databricks

Kubernetes

Docker

Terraform

Job description

About the Role

CloudAI Technologies is looking for an AI Practice Lead to build and lead our AI engineering and Generative AI practice.

This is a senior technical leadership position combining AI architecture, hands‑on technical leadership, solution development, client engagement, team building, and practice development.

The AI Practice Lead will define how CloudAI designs and delivers enterprise AI solutions, including Generative AI, agentic AI, RAG, enterprise search, machine learning, intelligent automation, and AI-enabled applications.

Key Responsibilities

• Define and execute CloudAI Technologies' AI practice strategy, architecture standards, engineering methodologies, and technology roadmap.
• Architect enterprise AI and Generative AI solutions across AWS, Azure, and modern AI platforms.
• Lead development of LLM-powered applications, RAG systems, AI agents, multi-agent workflows, copilots, and intelligent automation solutions.
• Design enterprise AI architectures covering models, data, vector/search infrastructure, APIs, orchestration, security, observability, and applications.
• Evaluate foundation models, AI platforms, frameworks, and emerging technologies based on business and technical requirements.
• Establish patterns for prompt engineering, context engineering, tool/function calling, agent orchestration, model evaluation, and guardrails.
• Define approaches for RAG, embeddings, vector search, knowledge retrieval, structured and unstructured enterprise data integration.
• Establish LLMOps/MLOps, evaluation, monitoring, governance, security, and responsible AI practices.
• Lead technical discovery and architecture workshops with customers.
• Translate business requirements into practical AI solutions and implementation roadmaps.
• Develop prototypes and proofs of concept and guide teams in converting successful prototypes into production-grade solutions.
• Provide architecture and code reviews for critical AI implementations.
• Build reusable AI accelerators, frameworks, reference architectures, and solution components.
• Mentor and provide technical direction to AI engineers, data engineers, and software engineers.
• Participate in hiring and capability development for the AI practice.
• Support proposals, estimates, solution presentations, and technical discussions with prospective customers.
• Partner with CloudAI's Cloud, Data, and Application practices to deliver integrated solutions.

Required Qualifications

• 10+ years of technology/software/data experience with significant recent experience delivering AI/ML solutions.
• Strong hands‑on understanding of Generative AI and LLM application architecture.
• Strong Python development skills.
• Experience building production applications using major LLM platforms and APIs.
• Experience with RAG, embeddings, vector databases/search, agentic workflows, and enterprise knowledge systems.
• Strong understanding of machine learning and AI fundamentals.
• Experience with AWS and/or Azure AI ecosystems.
• Understanding of modern data architectures, APIs, microservices, and cloud-native application development.
• Experience taking AI solutions from prototype through production.
• Strong understanding of AI security, data privacy, governance, observability, and evaluation.
• Ability to lead technical teams while remaining sufficiently hands‑on to evaluate architecture and engineering quality.
• Strong client‑facing communication and solutioning capabilities.

Preferred Qualifications

Experience with platforms and technologies such as Azure OpenAI, Amazon Bedrock, Azure AI Foundry, Databricks, Snowflake, MLflow, LangChain/LangGraph, LlamaIndex, vector databases, Kubernetes, Docker, Terraform, and enterprise data/catalog platforms is desirable.

Experience building AI SaaS products, enterprise agents, natural-language-to-data solutions, semantic layers, or AI-assisted software engineering platforms would be particularly valuable.