Role: AI/ML Engineer – Lead/Architect-15 years

Contract

REMOTE

Lead the end-to-end execution of high-priority AI/ML projects, ensuring they are delivered on time, within budget, and to the highest technical standards.

Translate the enterprise AI strategy and product roadmaps into detailed project plans, technical specifications, and actionable backlogs for engineering teams.

Serve as the primary technical point of contact for project stakeholders, managing dependencies, mitigating risks, and communicating progress effectively.

RAG Decision Clarity- Ability to design, implement, and explain complete GenAI/RAG solutions independently, from business problem to production deployment, strong understanding of when to use RAG and when not to, ability to justify design choices instead of using GenAI by default.

Embedding & Vector Search Expertise – Hands-on experience with embedding models, vector dimensions, similarity metrics, and internal workings of vector databases.

Chunking & Context preservation- expertise in multiple chunking strategies with clear understanding of context loss and mitigation techniques.

Metadata vs Semantic search Understanding- clear distinction between metadata-based filtering and semantic retrieval, and ability to apply each appropriately.

Agentic Architecture – Ability to design agent workflows with clearly defined responsibilities, avoids unnecessary or misaligned use of agents.

Multimodal document processing- experience in handling texts, tables, and images.

AI Governance & AIRB Facilitation:

Manage the day-to-day operations of the AI Review Board (AIRB) submission process, acting as a hands-on guide for Data Science and product teams.

Facilitate the preparation of all required documentation for AIRB reviews, ensuring submissions are complete, clear, and proactively address potential ethical, compliance, and technical concerns.

Implement and enforce the governance framework, ensuring teams adhere to established standards and best practices for responsible AI.

Team Leadership & Technical Mentorship:

Provide direct line management, technical leadership, and mentorship to a team of senior AI/ML Engineers and Data Scientists.

Foster a culture of engineering excellence, collaboration, and continuous improvement within the team and enterprise.

Conduct code reviews, design sessions, and technical deep dives to ensure the quality, scalability, and robustness of AI solutions.

Hands-on MLOps & Engineering Practice:

Drive the practical implementation of the MLOps strategy, directly overseeing the construction and optimization of CI/CD pipelines for AI/ML systems using tools like GitHub Actions.

Enforce rigorous engineering hygiene, including version control for code, data, and models (Git, DVC), and the application of Infrastructure as Code (IaC) principles.

Lead the technical implementation of production monitoring solutions to track model performance, identify drift, and ensure the long-term reliability of deployed AI systems.