AIML Engineer

Primary Responsibilities:

AI Project Execution & Delivery:

• 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.

Required Qualifications:

• Proven AI/ML Leadership: 10-15 years of experience in the AI/ML field with at least 4-5 years in a leadership or management role leading technical teams in the delivery of complex AI solutions.
• Experience with AI Governance: Direct hands-on experience successfully navigating an internal AI ethics risk or governance review process for multiple projects.
• Strong Project Management Skills: Demonstrated ability to manage complex technical projects from conception to deployment with expertise in agile methodologies.
• Expertise in the ML Lifecycle: Deep practical knowledge of the entire machine learning lifecycle from data acquisition and feature engineering to model deployment and post-launch monitoring.
• Hands-on MLOps Experience: Proven experience building and managing CI/CD pipelines and MLOps workflows for machine learning.
• Strong Technical Foundation: Proficient in Python common ML frameworks (e.g. TensorFlow PyTorch scikit-learn) and cloud platforms (AWS Azure or GCP).

Preferred Qualifications:

• Advanced Degree: A Masters or Ph.D. in a relevant quantitative field.
• Healthcare Domain Experience: Experience developing and deploying AI/ML solutions within a healthcare or other highly regulated environment.
• Product Mindset: Experience working closely with product managers to define and deliver AI-powered features and products.
• Expertise in GenAI Operations: Specific experience in the operational challenges of deploying and managing LLM-based applications in production.
• Mentorship and Talent Development: A passion for coaching and developing technical talent with a track record of growing senior engineers into tech leads.