Ai Architect

Key Responsibilities:

Strategy & Roadmap

• Define and drive the AI strategy, aligning with business goals and innovation priorities.

• Develop and maintain the AI solution roadmap, including short-term deliverables and long-term vision.

• Evaluate emerging AI trends and technologies to inform strategic direction.

Architecture & Design

• Architect end-to-end AI solutions using Gen AI, Agentic AI, LLMs, and multi-modal AI.

• Design intelligent agent systems using LangChain, LangGraph, Model Context Protocol (MCP), and Agent to Agent Protocols.

• Establish scalable and modular AI architectures that support RAG pipelines, Vector DBs, and Embeddings.

• Define and enforce AI governance frameworks, including Responsible AI, GuardRails, and compliance with AI Ethics & Regulations.

Assessment & Optimization

• Conduct technical assessments of existing AI/ML systems, models, and data pipelines.

• Identify gaps, risks, and opportunities for modernization or enhancement.

• Recommend architectural improvements and integration strategies for legacy systems.

Deployment & Integration

• Lead deployment of AI models using Docker, Kubernetes, and MLOps best practices.

• Integrate AI solutions with enterprise platforms and cloud-native services (Azure, AWS, GCP).

• Ensure performance, scalability, and security of deployed AI systems.

Leadership & Collaboration

• Collaborate with product owners, data scientists, engineers, and business stakeholders.

• Mentor engineering teams and contribute to talent development in AI and ML domains.

• Represent AI architecture in enterprise governance forums and technical councils.

Must-Have Skills:

• Generative AI (Gen AI), Agentic AI

• AI Strategy, Architecture, and Roadmap Planning

• Python Programming

• AI Frameworks (LangChain, AutoGen, CrewAI and LangGraph)

• Model Context Protocol (MCP), Agent to Agent Protocol

• GuardRails, AI Ethics and Regulations

• Prompt Engineering, Responsible AI

• Distillation, RAG, Fine-tuning

• Multi-modal AI, LLMs

• Vector Databases, Embeddings

• GenAI deployment tools (Docker, Kubernetes)

• AI Solution Assessment and Optimization