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

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)