AI Architect

Overview

Job Description:
Technical Leadership & Architecture
● Design and architect end-to-end AI solutions spanning traditional ML, generative AI, and Agentic AI systems
● Evaluate and select appropriate design patterns for GenAI implementations, clearly articulating tradeoffs between approaches (RAG vs fine-tuning, prompt engineering strategies, agent orchestration patterns, etc.)
● Define and implement comprehensive evaluation frameworks for GenAI systems, including quality metrics, performance benchmarks, and responsible AI considerations
● Stay current with emerging AI/ML developments and assess their practical applicability to client environments
Client Engagement & Stakeholder Management
● Serve as trusted technical advisor to clients, translating complex AI concepts into business value
● Lead solution workshops and technical pre-sales presentations with C-level executives and technical teams
● Build and maintain strong client relationships through confident communication and delivery excellence
● Bridge the gap between cutting-edge AI capabilities and pragmatic, implementable solutions
Team Leadership & Delivery
● Drive technical teams to deliver high-quality AI solutions on time and within scope
● Provide hands-on technical guidance and code reviews, leading by example
● Manage delivery timelines and proactively identify and mitigate risks
● Mentor team members on AI best practices, design patterns, and implementation approaches
Hands-On Development
● Contribute directly to architecture and code implementation when needed
● Build proof-of-concepts and prototypes to validate technical approaches
● Debug complex technical issues across the AI stack
● Ensure code quality, scalability, and maintainability standards

Requirements

● Required Qualifications
○ 8+ years of experience in AI/ML engineering and architecture
○ Proven track record of implementing ML models in commercial production environments
○ Deep expertise in traditional machine learning (supervised/unsupervised learning, feature engineering, model optimization)
○ Significant experience with Generative AI technologies (LLMs, prompt engineering, RAG, fine-tuning, vector databases)
○ Hands-on experience building agentic AI systems and multi-agent architectures
○ Strong programming skills in Python and relevant ML/AI frameworks (SKlearn, XGBoost, PyTorch, TensorFlow, LangChain, LlamaIndex, etc.) ○ Demonstrated ability to design and implement GenAI evaluation
○ Excellent communication skills with ability to present complex technical topics clearly to both technical and non-technical audiences
○ Strong project management capabilities with history of delivering complex projects on schedule

● Preferred Qualifications
○ Experience with enterprise AI platform development and MLOps practices
○ Knowledge of AI governance frameworks and responsible AI practices
○ Familiarity with cloud platforms (AWS, Azure, GCP) and their AI/ML services
○ Experience with real-time AI systems and low-latency architectures
○ Background in telecommunications, financial services, or other regulated industries
○ Advanced degree in Computer Science, AI/ML, or related technical field

● Key Competencies
○ Technical Excellence
■ Expert understanding of GenAI design pattern tradeoffs (RAG architectures, agent frameworks, tool use, memory systems)
■ Proficiency in GenAI evaluation methodologies (automated metrics, LLM-as-judge, human evaluation)
■ Strong foundation in traditional ML fundamentals and deployment patterns
○ Leadership & Delivery
■ Ability to drive teams toward concrete deliverables while maintaining quality standards
■ Experience managing multiple stakeholders and competing priorities
■ Track record of delivering complex technical projects in client environments
○ Communication & Influence
■ Confident, clear communication style suitable for executive engagement
■ Ability to build credibility quickly with technical and business stakeholders
■ Skill in translating technical complexity into actionable business insights
○ Mindset & Approach
■ Continuous learning orientation with pulse on latest AI developments
■ Pragmatic decision-making that balances innovation with implementability
■ Client-centric mindset focused on delivering measurable business value
■ Comfortable with ambiguity and able to structure unstructured problems

Work Set-Up: Hybrid in BGC (1-2x a week RTO or as needed)