Primary Responsibilities:

Lead AI/ML Engineer

Al 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 Al 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 GenAl 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 image

Required Qualifications:

Proven AI/ML Leadership: 7-9 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 Al solutions.
Experience with Al Governance: Direct, hands-on experience successfully navigating an internal Al 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: