AI Architect

Role Overview

Our client, a leading technology-driven organization, is seeking an experienced AI Architect to lead the design, development, and delivery of advanced artificial intelligence solutions. This role combines deep technical expertise with stakeholder engagement and team leadership to drive impactful AI initiatives across diverse environments.

Key Responsibilities

Technical Architecture & Strategy

• Design and implement end-to-end AI solutions across traditional machine learning, generative AI, and agent-based systems
• Assess and recommend appropriate architectural approaches for generative AI cases (e.g., retrieval-augmented generation, fine-tuning, prompt strategies, and agent orchestration)
• Establish evaluation frameworks to measure system quality, performance, and responsible AI compliance 
• Monitor emerging AI/ML trends and determine their practical application within business contexts

Stakeholder Engagement & Advisory

• Act as a technical advisor, translating complex AI concepts into business-relevant insights.
• Facilitate workshops, solution discussions, and technical presentations for both executive and technical audiences
• Build strong stakeholder relationships through effective communication and delivery
• Align advanced AI capabilities with realistic, implementable business solutions

Team Leadership & Delivery Management

• Lead technical teams in delivering high-quality AI solutions within defined timelines and scope.

• Provide hands-on guidance, including architecture direction and code reviews

• Identify risks proactively and implement mitigation strategies

• Mentor team members on AI methodologies, design practices, and implementation standards

Hands-On Development

• Contribute to development activities, including architecture implementation and coding when required
• Develop prototypes and proof-of-concepts to validate solution approaches
• Troubleshoot and resolve complex technical challenges across the AI stack
• Ensure solutions meet standards for scalability, maintainability, and performance

Qualifications

Required:

• Minimum of 8 years of experience in AI/ML engineering and architecture

• Proven experience deploying machine learning models in production environments 

• Strong expertise in core machine learning concepts (e.g., supervised/unsupervised learning, feature engineering, model optimization)

• Hands-on experience with generative AI technologies, including large language models, prompt engineering, retrieval-augmented generation, and fine-tuning

• Experience designing and implementing agent-based or multi-agent AI systems

• Proficiency n Python and common AI/ML frameworks (e.g., scikit-learn, XGBoost, PyTorch, TensorFlow, and similar tools)

• Experience designing evaluation approaches for generative AI systems

• Strong communication skills with the ability to explain complex concepts varied audiences 
• Demonstrated project delivery experience in complex technical environments

Preferred:

• Experience with enterprise AI platforms and MLOps practices

• Familiarity with AI governance and responsible AI frameworks

• Knowledge of major cloud platforms and their AI/ML capabilities

• Experience building real-time or low-latency AI systems

• Industry exposure to regulated environments such as telecommunications or financial services

• Advanced degree in Computer Science, Artificial Intelligence, or a related field



Core Competencies
Technical Expertise

• Deep understanding of generative AI architectures and design trade-offs

• Knowledge of evaluation methodologies, including automated and human-in-the-loop approaches

• Strong foundation in machine learning lifecycle and deployment strategies

Leadership & Execution

• Ability to guide teams toward successful delivery while maintaining quality standards

• Experience managing multiple stakeholders and competing priorities

• Proven track record in delivering complex, client-facing projects

Communication & Influence

• Confident communicator capable of engaging senior stakeholders

• Ability to simplify technical complexity into actionable insights

• Strong relationship-building and stakeholder management skills

Mindset & Approach

• Continuous learner with strong awareness of evolving AI technologies

• Practical decision-maker balancing innovation with feasibility

• Results-oriented with a focus on delivering measurable business value

• Comfortable navigating ambiguity and structuring complex problems