AI Architect

Required Skills:

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)

Required Education:

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