AI Architect

Key Responsibilities

AI Architecture & Solution Design

• Design and architect enterprise-grade AI solutions leveraging Generative AI and Large Language Models.
• Define architecture for LLM-based systems, agentic workflows, retrieval-augmented generation (RAG), and AI copilots.
• Evaluate and select appropriate models, frameworks, and infrastructure for production AI systems.
• Ensure scalability, reliability, and performance of deployed AI solutions.

Machine Learning & Model Expertise

• Provide deep technical expertise in:
  • Large Language Models (LLMs)
  • Transformer architectures
  • Generative AI techniques
  • Model evaluation and benchmarking
• Design approaches for fine-tuning, prompt engineering, and model adaptation.
• Guide teams on best practices in ML pipelines, experimentation, and model lifecycle management.

Production Deployment & MLOps

• Lead deployment of machine learning and GenAI systems into production environments.
• Architect and implement MLOps pipelines, model monitoring, and continuous improvement processes.
• Ensure AI systems are secure, scalable, and operationally maintainable.

AI Governance & Responsible AI

• Implement frameworks for:
  • AI governance
  • Model explainability
  • Transparency
  • Risk management
• Ensure compliance with enterprise AI governance standards and regulatory expectations.
• Define policies for model validation, bias mitigation, and responsible deployment.

Client Engagement & Technical Leadership

• Act as a trusted technical advisor to client stakeholders.
• Clearly communicate complex AI concepts to executives, architects, and engineering teams.
• Represent the company with credibility in technical and strategic discussions around AI adoption.
• Work closely with client teams to translate business problems into AI-driven solutions.

Research & Innovation

• Stay current with emerging developments in:
  • Generative AI
  • Large language models
  • AI agents and agentic architectures
  • AI infrastructure and tooling
• Evaluate new research and technologies to determine their practical applicability in enterprise environments.
• Help shape the organization’s AI strategy and technical direction.

Required Qualifications

• Master’s degree in Data Science, Machine Learning, Computer Science, or related field.
• Strong expertise in machine learning fundamentals and modern generative AI technologies.
• Proven experience designing and deploying AI/ML systems in production environments.
• Deep knowledge of:
  • Large Language Models
  • Generative AI architectures
  • ML pipelines and model lifecycle management
• Experience working with AI frameworks and ecosystems used for building GenAI applications.
• Experience implementing AI governance, explainability, and responsible AI practices.
• Strong understanding of enterprise software architecture and distributed systems.

Preferred Qualifications

• Experience with agentic AI systems and orchestration frameworks.
• Experience building RAG-based AI systems.
• Familiarity with AI platform engineering and scalable AI infrastructure.
• Contributions to AI research, open-source projects, or technical publications.
• Experience working with enterprise clients in regulated industries.

Key Skills

Technical Skills

• Machine Learning & Data Science
• Large Language Models (LLMs)
• Generative AI systems
• AI agents and agentic architectures
• MLOps and model lifecycle management
• AI governance and explainability

Professional Skills

• Strong analytical and problem-solving capabilities
• Excellent communication and presentation skills
• Ability to simplify complex AI concepts for diverse audiences
• Collaborative mindset and ability to work effectively within teams
• Client-facing professionalism and credibility

Work Environment

• Full-time in-office role with collaboration across engineering and client teams.
• Weekly client visits for workshops, architecture discussions, and solution design sessions.
• High collaboration with data scientists, engineers, architects, and business stakeholders