Sr. AI/ML Engineer

• Hands-on experience in building and maintaining Knowledge graphs and Recommendation systems knowledge
• Lead the design and deployment of scalable AI/ML solutions focused on real-time personalization, recommendation systems, and customer knowledge graphs, driving measurable improvements in engagement and conversion.

Responsibilities:

· Design and build collaborative, content-based, and hybrid recommendation systems

· Develop real-time personalization pipelines and ranking models

· Architect end-to-end ML systems (batch + streaming) with low-latency inference

· Build customer knowledge graphs (Neo4j/Neptune) modeling users, products, and interactions

· Enable Customer 360 insights and context-aware recommendations

· Develop scalable pipelines using Python, Spark, Kafka Implement feature engineering, model training, and deployment workflows

· Drive experimentation (A/B testing) and optimize for CTR, engagement, and conversion Ensure data quality, model performance, and system reliability

· Apply MLOps practices (CI/CD, monitoring, model lifecycle management)

· Mentor team members and collaborate with product/business stakeholders

· Python and experience with Pandas, PySpark Expertise in recommender systems (matrix factorization, deep learning, ranking models)

· Experience in entity resolution / record linkage

· Hands-on with graph modeling & graph databases (Neo4j, RDF, graph embeddings)

· Strong understanding of ML lifecycle, experimentation, and evaluation metrics (NDCG, MAP, Precision/Recall)

Nice to Have :

· Experience with real-time ML systems and large-scale data (TB/PB)

· Impact Drive personalized customer experiences, improve engagement & conversion, and enable data-driven decision-making at scale.