Job Description

Responsibilities:

• Architect and lead the development of AI models that integrate multimodal data (text + image) for product classification, attribute extraction, and vision-text consistency validation.
• Design and evolve taxonomy intelligence frameworks, including canonical entity graphs, category hierarchies, and attribute contracts for retail product data.
• Build and optimize CLIP-style embedding models to assess image-category similarity and detect mismatches between product visuals and descriptions.
• Design scalable LLM-based pipelines for zero/few-shot classification, brand normalization, and policy enforcement across diverse product types.
• Lead the creation of graph-based reasoning systems for alias resolution, brand variants, and hierarchical relationships (e.g., “Yoga” → “Lenovo Yoga”).
• Collaborate with data engineers, search relevance teams, and business stakeholders to ensure taxonomy and multimodal intelligence are tightly integrated into the search and ranking pipeline.
• Guide the development of data quality scoring systems using multimodal confidence metrics and taxonomy correctness.
• Mentor a team of AI scientists and engineers, providing technical leadership, code reviews, and strategic direction.
• Drive experimentation and active learning strategies to continuously improve model performance and reduce manual review load.
• Ensure scalability and cost-efficiency through model distillation, caching, and tiered inference strategies.

Qualifications:

• Degree in Computer Science, Machine Learning, Computational Linguistics, or a related field.
• 12+ years of experience in applied AI/ML, with a strong focus on multimodal learning, taxonomy modeling, or knowledge graphs.
• Deep expertise in LLMs (e.g., GPT, T5, LLaMA) and VLMs (e.g., CLIP, BLIP, Flamingo) for classification, embedding generation, and cross-modal alignment.
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, OpenCLIP.
• Hands-on experience with graph databases (e.g., Neo4j, Amazon Neptune) and graph algorithms for entity resolution and taxonomy mapping.
• Strong understanding of semantic similarity, embedding-based retrieval, and zero-shot learning techniques.
• Good to have - experience deploying models in production using MLOps tools (e.g., MLflow, Kubeflow, SageMaker).
• Familiarity with retail product data, e-commerce taxonomies, and consumer search behavior.
• Proven leadership in guiding cross-functional teams and mentoring junior scientists.
• Excellent communication and stakeholder engagement skills, with the ability to translate complex AI concepts into business impact.

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• Provides technical and strategic input during the project planning phase in the form of technical architectural designs and recommendation
• Collaborates with all relevant parties in order to review the objectives and constraints of solutions and determine conformance with the Enterprise Architecture
•  Identifies implementation risks and potential impacts

2.Enable Delivery Teams by providing optimal delivery solutions/ frameworks

• Build and maintain relationships with executives, technical leaders, product owners, peer architects and other stakeholders to become a trusted advisor
• Develops and establishes relevant technical, business process and overall support metrics (KPI/SLA) to drive results
• Manages multiple projects and accurately reports the status of all major assignments while adhering to all project management standards
• Identify technical, process, structural risks and prepare a risk mitigation plan for all the projects
• Ensure quality assurance of all the architecture or design decisions and provides technical mitigation support to the delivery teams
• Recommend tools for reuse, automation for improved productivity and reduced cycle times
• Leads the development and maintenance of enterprise framework and related artefacts
• Develops trust and builds effective working relationships through respectful, collaborative engagement across individual product teams
• Ensures architecture principles and standards are consistently applied to all the projects
• Ensure optimal Client Engagement
  • Support pre-sales team while presenting the entire solution design and its principles to the client
  • Negotiate, manage and coordinate with the client teams to ensure all requirements are met and create an impact of solution proposed
  • Demonstrate thought leadership with strong technical capability in front of the client to win the confidence and act as a trusted advisor